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• v.1(1); Jan-Mar 2010

Evolution of Clinical Research: A History Before and Beyond James Lind

Dr arun bhatt.

President, Clininvent Research Pvt Ltd, Mumbai, India

The evolution of clinical research traverses a long and fascinating journey. From the first recorded trial of legumes in biblical times to the first randomized controlled of trial of streptomycin in 1946, the history of clinical trial covers a wide variety of challenges - scientific, ethical and regulatory. The famous 1747 scurvy trial conducted by James Lind contained most elements of a controlled trial. The UK Medical Research Council's (MRC) trial of patulin for common cold in 1943 was the first double blind controlled trial. This paved the way for the first randomized control trial of streptomycin in pulmonary tuberculosis carried out in 1946 by MRC of the UK. This landmark trial was a model of meticulousness in design and implementation, with systematic enrolment criteria and data collection compared with the ad hoc nature of other contemporary research. Over the years, as the discipline of controlled trials grew in sophistication and influence, the streptomycin trial continues to be referred to as ground breaking. The ethical advances in human protection include several milestones - Nuremberg Code, Declaration of Helsinki, Belmont Report, and 1996, International Conference on Harmonization Good Clinical Practice guidance. In parallel to ethical guidelines, clinical trials started to become embodied in regulation as government authorities began recognizing a need for controlling medical therapies in the early 20th century. As the scientific advances continue to occur, there will be new ethical and regulatory challenges requiring dynamic updates in ethical and legal framework of clinical trials.

“The charm of history and its enigmatic lesson consist in the fact that, from age to age, nothing changes and yet everything is completely different.” - Aldous Huxley

The evolution of clinical research traverses a long and fascinating journey. The recorded history of clinical trials goes back to the biblical descriptions in 500 BC. The journey moves from dietary therapy – legumes and lemons – to drugs. After basic approach of clinical trial was described in 18th century, the efforts were made to refine the design and statistical aspects. These were followed by changes in regulatory and ethics milieu. This article captures the major milestones in the evolution of clinical trials.

562 BC - 1537: Pre-James Lind Era

The world's first clinical trial is recorded in the “Book of Daniel” in The Bible. 1 This experiment resembling a clinical trial was not conducted by a medical, but by King Nebuchadnezzar a resourceful military leader. 1 During his rule in Babylon, Nebuchadnezzar ordered his people to eat only meat and drink only wine, a diet he believed would keep them in sound physical condition. 1 But several young men of royal blood, who preferred to eat vegetables, objected. The king allowed these rebels to follow a diet of legumes and water — but only for 10 days. When Nebuchadnezzar's experiment ended, the vegetarians appeared better nourished than the meat-eaters, so the king permitted the legume lovers to continue their diet. 1 This probably was the one of the first times in evolution of human species that an open uncontrolled human experiment guided a decision about public health.

Avicenna (1025 AD) in his encyclopedic ‘Canon of Medicine’ describes some interesting rules for the testing of drugs. 2 He suggests that in the clinical trial a remedy should be used in its natural state in disease without complications. He recommends that two cases of contrary types be studied and that study be made of the time of action and of the reproducibility of the effects. 2 These rules suggest a contemporary approach for clinical trials. However, there seems to be no record of the application of these principles in practice.

The first clinical trial of a novel therapy was conducted accidentally by the famous surgeon Ambroise Pare in 1537. 1 , 3 In 1537 while serving with the Mareschal de Motegni he was responsible for the treatment of the battlefield wounded soldiers. As the number of wounded was high and the supply of conventional treatment – oil was not adequate to treat all the wounded, he had to resort to unconventional treatment. He describes,' at length my oil lacked and I was constrained to apply in its place a digestive made of yolks of eggs, oil of roses and turpentine. That night I could not sleep at any ease, fearing that by lack of cauterization I would find the wounded upon which I had not used the said oil dead from the poison. I raised myself early to visit them, when beyond my hope I found those to whom I had applied the digestive medicament feeling but little pain, their wounds neither swollen nor inflamed, and having slept through the night. The others to whom I had applied the boiling oil were feverish with much pain and swelling about their wounds. Then I determined never again to burn thus so cruelly the poor wounded by arquebuses’. 2 However, it would take another 200 years before a planned controlled trial would be organized.

1747: James Lind and Scurvy Trial

James Lind is considered the first physician to have conducted a controlled clinical trial of the modern era. 1 – 4 Dr Lind (1716-94), whilst working as a surgeon on a ship, was appalled by the high mortality of scurvy amongst the sailors. He planned a comparative trial of the most promising cure for scurvy. 1 – 4 His vivid description of the trial covers the essential elements of a controlled trial.

Lind describes“”On the 20th of May 1747, I selected twelve patients in the scurvy, on board the Salisbury at sea. Their cases were as similar as I could have them. They all in general had putrid gums, the spots and lassitude, with weakness of the knees. They lay together in one place, being a proper apartment for the sick in the fore-hold; and had one diet common to all, viz. water gruel sweetened with sugar in the morning; fresh mutton-broth often times for dinner; at other times light puddings, boiled biscuit with sugar, etc., and for supper, barley and raisins, rice and currants, sago and wine or the like. Two were ordered each a quart of cyder a day. Two others took twenty-five drops of elixir vitriol three times a day … Two others took two spoonfuls of vinegar three times a day … Two of the worst patients were put on a course of sea-water … Two others had each two oranges and one lemon given them every day … The two remaining patients, took … an electary recommended by a hospital surgeon … The consequence was, that the most sudden and visible good effects were perceived from the use of oranges and lemons; one of those who had taken them, being at the end of six days fit for duty … The other was the best recovered of any in his condition; and … was appointed to attend the rest of the sick. Next to the oranges, I thought the cyder had the best effects …” (Dr James Lind's “Treatise on Scurvy” published in Edinburgh in 1753)

Although the results were clear, Lind hesitated to recommend the use of oranges and lemons because they were too expensive. 3 It was nearly 50 years before the British Navy eventually made lemon juice a compulsory part of the seafarer's diet, and this was soon replaced by lime juice because it was cheaper.

Lind's Treatise of 1953, was written while he was resident in Edinburgh and a Fellow of the Royal College of Physicians, contains not only his well known description of a controlled trial showing that oranges and lemons were dramatically better than the other treatments for the disease, but also a systematic review of previous literature on scurvy. 5

In 2003, Royal College of Physicians established The James Lind Library to commemorate 250 th anniversary of publication of Dr Lind's pioneering contribution “Treatise on Scurvy”. The James Lind Library ( www.jameslindlibrary.org ) was created to improve public and professional general knowledge about fair tests of treatments in healthcare and their history. 5 This library is a website ( www.jameslindlibrary.org ) that introduces visitors to the principles of fair tests of treatments, with a series of short, illustrated essays. In 2003, Scientific American awarded the Library a Sci/Tech web award. The publicity and popularity of the James Lind Library has made 20 May to be designated International Clinical Trials Day, because James Lind's celebrated controlled trial began on that day in 1747. 5

1800: Arrival of Placebo

It took another century before the emergence of another important mile stone in the history of modern clinical trial: the placebo. The word placebo first appeared in medical literature in the early 1800s. 1 Hooper's Medical Dictionary of 1811 defined it as “an epithet given to any medicine more to please than benefit the patient.” However, it was only in 1863 that United States physician Austin Flint planned the first clinical study comparing a dummy remedy to an active treatment. He treated 13 patients suffering from rheumatism with an herbal extract which was advised instead of an established remedy. In 1886, Flint described the study in his book A Treatise on the Principles and Practice of Medicine. “This was given regularly, and became well known in my wards as the ‘placeboic remedy’ for rheumatism. The favorable progress of the cases was such as to secure for the remedy generally the entire confidence of the patients.”

1943: The First Double blind Controlled Trial - Patulin for Common Cold

The Medical Research Council (MRC) UK carried out a trial in 1943-4 to investigate patulin treatment for (an extract of Penicillium patulinum) the common cold. 6 This was the first double blind comparative trial with concurrent controls in the general population in recent times. 6 It was one of the last trial with non-randomized or quasi-randomized allocation of subjects. 6 The MRC Patulin Clinical Trials Committee (1943) was chaired by Sir Harold Himsworth, and its statisticians were M Greenwood and W J Martin. This nationwide study enrolled over a thousand British office and factory workers suffering from colds. This was quite a challenging endeavor in wartime,

The study was rigorously controlled by keeping the physician and the patient blinded to the treatment. The treatment allocation was done using an alternation procedure. A nurse allocated the treatment in strict rotation in a separate room. The nurse filed the record counterfoil separately, and detached the code label for the appropriate bottle before asking the patient to visit the doctor. 6 The statisticians considered this an effective random concurrent allocation. .However, the outcome of the trial was disappointing as the analysis of trial data did not show any protective effect of patulin. 6

1946 First Randomized Curative Trial - The Randomized Controlled Trial of Streptomycin

The idea of randomization was introduced in 1923. However, the first randomized control trial of streptomycin in pulmonary tuberculosis was carried out in 1946 by MRC of the UK. 6 , 7 The MRC Streptomycin in Tuberculosis Trials Committee (1946) was chaired by Sir Geoffrey Marshall, and the statistician was Sir Austin Bradford Hill and Philip Hart, who later directed the MRC's tuberculosis research unit, served as secretary. Marc Daniels, as the “registrar” coordinated the clinicians at the participating hospitals. The trial began in 1947. As the amount of streptomycin available from US was limited, it was ethically acceptable for the control subjects to be untreated by the drug—a statistician's dream. 6 This trial was a model of meticulousness in design and implementation, with systematic enrolment criteria and data collection compared with the ad hoc nature of other contemporary research 8 A key advantage of Dr Hill's randomization scheme over alternation procedure was “allocation concealment” at the time patients were enrolled in the trial. Another significant feature of the trial was the use of objective measures such as interpretation of x-rays by experts who were blinded to the patient's treatment assignment. 8

Sir Bradford Hill had formed his allocation ideas over several years (with randomisation replacing alternation in order to better conceal the allocation schedule), but had only tried them out in disease prevention. Dr Hill instituted randomization – a new statistical process which has been described in detail in the landmark BMJ paper of 1948. 7

“Determination of whether a patient would be treated by streptomycin and bed-rest (S case) or by bed-rest alone (C case) was made by reference to a statistical series based on random sampling numbers drawn up for each sex at each centre by Professor Bradford Hill; the details of the series were unknown to any of the investigators or to the co--coordinator and were contained in a set of sealed envelopes, each bearing on the outside only the name of the hospital and a number. After acceptance of a patient by the panel, and before admission to the streptomycin centre, the appropriate numbered envelope was opened at the central office; the card inside told if the patient was to be an S or a C case, and this information was then given to the medical officer of the centre. Patients were not told before admission that they were to get special treatment. C patients did not know throughout their stay in hospital that they were control patients in a special study; they were in fact treated as they would have been in the past, the sole difference being that they had been admitted to the centre more rapidly than was normal. Usually they were not in the same wards as S patients, but the same regime was maintained

Sir Bradford Hill had been anxious that physicians would be unwilling to give up the doctrine of anecdotal experience. However, the trial quickly became a model of design and implementation and gave a boost to Dr Hill's views and subsequent teaching, and resulted, after some years, in the present virtually universal use of randomised allocation in clinical trials. 6 The greatest influence of this trial lay in its methods which have affected virtually every area of clinical medicine. 8 Over the years, as the discipline of controlled trials grew in sophistication and influence, the streptomycin trial continues to be referred to as ground breaking. 8

Evolution of Ethical and Regulatory Framework

The ethical framework for human subject protection has its origins in the ancient Hippocratic Oath, which specified a prime duty of a physician – to avoid harming the patient. However, this oath was not much respected in human experimentation and most advances in protection for human subjects have been a response to human abuses e.g. World War II experiments.

The first International Guidance on the ethics of medical research involving subjects – the Nuremberg Code was formulated in 1947. Although informed consent for participation in research was described in 1900, the Nuremberg Code highlighted the essentiality of voluntariness of this consent. 9 In 1948, Universal Declaration of Human Rights (adopted by the General Assembly of the United Nations) expressed concern about rights of human beings being subjected to involuntary maltreatment. 9 The brush with thalidomide tragedy helped the U.S. pass the 1962 Kefauver-Harris amendments, which strengthened federal oversight of drug testing and included a requirement for informed consent. 10

In 1964 at Helsinki, the World Medical Association articulated general principles and specific guidelines on use of human subjects in medical research, known as the Helsinki Declaration. The Helsinki Declaration has been undergoing changes every few years the last one being in 2008. However, the use of placebo and post-trial access continue to be debatable issues.

In 1966, the International Covenant on Civil and Political Rights specifically stated, ‘No one shall be subjected to torture or to cruel, inhuman or degrading treatment or punishment. In particular, no one shall be subjected without his consent to medical or scientific treatment.’ 9 Dr. Henry Beecher's 1966 study of abuses and the discovery of human exploitation of Tuskegee study in the 1970s reinforced the call for tighter regulation of government funded human research. 10 The US National Research Act of 1974 and Belmont Report of 1979 were major efforts in shaping ethics of human experimentation. In 1996, International Conference on Harmonization published Good Clinical Practice, which has become the universal standard for ethical conduct of clinical trials.

In parallel to ethical guidelines, clinical trials started to become embodied in regulation as government authorities began recognizing a need for controlling medical therapies in the early 20th century. The FDA was founded in 1862 as a scientific institution and became a law enforcement organization after the US Congress passed the Food and Drugs Act in 1906. After that, legislation progressively demanded greater accountability for marketing food and drugs and the need for testing drugs in clinical trials increased. The regulatory and ethical milieu will continue to evolve as new scientific disciplines and technologies become part of drug development.

Evolution of Clinical Trials in India

India has recently been recognized as an attractive country for clinical trials. But the country's journey in clinical research field has a long history. India has a rich heritage of traditional medicine – Ayurveda. The classic ayurvedic texts contain detailed observations on diseases and in-depth guidance on remedies. It is likely that these descriptions are based on direct observations made by the ancient ayurveda experts. However, there is no recorded documentation in the ancient texts of any clinical experiments. Hence, one has to fall back on current history of medical research in India.

The major historic milestones of the Indian Council of Medical Research reflect, in many ways, the growth and development of medical research in the country over the last nine decades. First meeting of the Governing Body of the Indian Research Fund Association (IRFA) was held on November 15, 1911 at the Plague Laboratory, Bombay, under the Chairmanship of Sir Harcourt Butler. 11 At the 2nd meeting of the Governing Body in 1912, a historic decision was taken to start a journal for Indian Medical research. Between 1918--20, several projects on beriberi, malaria, kala azar and indigenous drugs were initiated. In 1945, a Clinical Research Unit – the first research unit of IRFA attached to a medical institution- was established at the Indian Cancer Research Centre, Bombay. In 1949, IRFA was redesignated as the Indian Council of Medical Research. Over next 60 years, ICMR established many national research centers in the fields of nutrition, tuberculosis, leprosy, viral disease, cholera, enteric disease, reproductive disorders, toxicology, cancer, traditional medicine, gas disaster, genetics, AIDS etc.

The Central Ethical Committee of ICMR on Human Research constituted under the Chairmanship of Hon'ble Justice (Retired) M.N. Venkatachaliah held its first meeting on September 10, 1996. Several subcommittees were constituted to consider ethical issues in specific areas e.g., Epidemiological Research; Clinical Evaluation of Products to be used on Humans; Organ Transplantation; Human Genetics, etc. The committee released Ethical Guidelines for Biomedical Research on Human Participants in 2000 which were revised in 2006. 9

Schedule Y of Drugs and Cosmetics Act came into force in 1988 and established the regulatory guidelines for clinical trial (CT) permission. The schedule did force the industry to conduct Phase III clinical trials for registration of a new drug and supported growth of a predominantly generic Indian pharmaceutical industry. However, this schedule only permitted clinical trials at a phase lower than its global status. This phase lag obstructed integration of India in global clinical development.

The next major step has been revision of Schedule Y in Jan 2005. 12 As compared to Schedule Y 1988, which had narrow and restrictive definitions of clinical trial phases, the amended Schedule Y 2005 provided pragmatic definitions for Phase I to IV. 12 The definitions and guidelines for clinical trial phases are broad and rational. The earlier restrictions on number patients and centers in early phases stipulated in Schedule Y 1988 were removed allowing the sponsor company freedom to decide these in relation to protocol requirements. The phase lag requirements gave way to acceptance of concurrent Phase II-III as part of global clinical trials.

Schedule Y 2005 legalized Indian GCP guidelines of 2001. This schedule stipulated GCP responsibilities of ethics committee (EC), investigator and sponsor and suggested formats for critical documents e.g. consent, report, EC approval, reporting of serious adverse event. These amendments in Schedule Y have been a major step forward in direction of GCP compliant trials and have provided the much-needed regulatory support to GCP guidelines.

Since the Scurvy trial, clinical trials have evolved into a standardized procedure, focusing on scientific assessment of efficacy and guarding the patient safety. As the discipline of drug development is enriched by novel therapies and technologies, there will always be a continuing need to balance medical progress and patient safety. As the scientific advances continue to occur, there will be new ethical and regulatory challenges requiring dynamic updates in ethical and legal framework of clinical trials.

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The Oxford Handbook of the History of Medicine

Mark Jackson is Professor of the History of Medicine at the University of Exeter and was Director of the Centre for Medical History there between 2000 and 2010. He served as Chair of the Wellcome Trust History of Medicine Funding Committee between 2003 and 2008 and is currently Chair of the Wellcome Trust Research Resources in Medical History Funding Committee. He has taught modules in the history of medicine and the history and philosophy of science for over twenty years at undergraduate and postgraduate levels to both medical and history students, and has also been involved in teaching medical history to GCSE and A-level students. His books include Newborn Child Murder (1996), The Borderland of Imbecility (2000), Infanticide: Historical Perspectives on Child Murder and Concealment 1550–2000 (ed., 2002), Allergy: The History of a Modern Malady (2006), Health and the Modern Home (ed., 2007), and Asthma: The Biography (2009). The Age of Stress: Science and the Search for Stability is due to be published by Oxford University Press in 2012.

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The Oxford Handbook of the History of Medicine celebrates the richness and variety of medical history around the world. In recent decades, the history of medicine has emerged as a rich and mature sub-discipline within history, but the strength of the field has not precluded vigorous debates about methods, themes, and sources. Bringing together over thirty international scholars, this book provides a constructive overview of the current state of these debates, and offers new directions for future scholarship. There are three sections: the first explores the methodological challenges and historiographical debates generated by working in particular historical ages; the second explores the history of medicine in specific regions of the world and their medical traditions, and includes discussion of the ‘global history of medicine’; the final section analyses, from broad chronological and geographical perspectives, both established and emerging historical themes and methodological debates in the history of medicine.

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The Beginning – Historical Aspects of Clinical Research, Clinical Research: Definitions, “Anatomy and Physiology,” and the Quest for “Universal Truth”

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To answer many of their clinical questions, health care practitioners need access to reports of original research. This requires the reader to critically appraise the design, conduct, and analysis of each study and subsequently interpret the results. This first chapter reviews some of the key historical developments that have led to the current paradigms used in clinical research, such as the concept of randomization, blinding (masking) and, placebo-controls.

Scientific inquiry is seeing what everyone else is seeing, but thinking of what no one else has thought A. Szentgyorgyi. 1873 (he won the Nobel Prize for isolating Vitamin C) [ 1 ].

Hulley S, Cummings S, Browner WS. Designing clinical research. 2nd ed. Philadelphia: Lippincott Williams & Wilkins; 2000.

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Windish DM, Hoot SJ, Green ML. Medicine residents’ understanding of the biostatistics and results in the medical literature. JAMA. 2007;298:1010–22.

Article   CAS   PubMed   Google Scholar  

Google Scholar  

Ahrens E. The crisis in clinical research: overcoming institutional obstacles. New York: Oxford University Press; 1992.

Fisher R. The design of experiments. Edinburgh: Oliver and Boyd; 1935.

Hart PD. Randomised controlled clinical trials. BMJ. 1992;302:1271–2.

Article   Google Scholar  

Amberson JB, MacMahon BT, Pinner M. A clinical trial of sanocrysin in pulmonary tuberculosis. Am Rev Tuber. 1931;24:401–35.

Hill AB. The clinical trial. Br Med Bull. 1951;7:278–82.

CAS   PubMed   Google Scholar  

White L, Tursky B, Schwartz G. Placebo: theory, research, and mechanisms. New York: Guilford Press; 1985.

Medical Research Council. Streptomycin treatment of pulmonary tuberculosis. BMJ. 1948;ii:769–82.

Thalidomide. http://en.wikipedia.org/wiki/Thalidomide

Institute of Medicine. Careers in clinical research: obstacles and opportunities. Washington, DC: National Academy Press; 1994.

DeMets DL, Califf RM. Lessons learned from recent cardiovascular clinical trials: Part I. Circulation. 2002;106:746–51.

Article   PubMed   Google Scholar  

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Glasser, S.P. (2014). The Beginning – Historical Aspects of Clinical Research, Clinical Research: Definitions, “Anatomy and Physiology,” and the Quest for “Universal Truth”. In: Glasser, S. (eds) Essentials of Clinical Research. Springer, Cham. https://doi.org/10.1007/978-3-319-05470-4_1

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The fascinating history of clinical trials

Professor of Biostatistics, University of South Australia

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Adrian Esterman does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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Clinical trials are under way around the world, including in Australia, testing COVID-19 vaccines and treatments .

These clinical trials largely fall into two groups. With observational studies , researchers follow a group of people to see what happens to them. With experimental studies , people are assigned to treatments, then followed.

These study designs have come about from centuries of people trying out different ways of treating people.

Here are some of the key moments in the history of clinical trials that led to the type of trials we see today for COVID-19.

Read more: From the research lab to your doctor's office – here's what happens in phase 1, 2, 3 drug trials

Ginseng in 11th-century China

One of the earliest observational studies occurred nearly 1,000 years ago in China. The 1061 Atlas of Materia Medica (Ben Cao Tu Jing) was compiled and edited by Song Su , a renowned scientist, administrator, diplomat and military strategist.

It documented a trial of ginseng:

[…] to evaluate the effect of genuine Shangdang ginseng, two persons were asked to run together. One was given the ginseng while the other ran without. After running for approximately three to five li [about 1,500-2,500 metres], the one without the ginseng developed severe shortness of breath, while the one who took the ginseng breathed evenly and smoothly.

This observational study is also the first recorded example of a control group .

A control group can be patients who are not treated at all, patients who receive a standard treatment compared to a new one, or patients who receive a placebo (a treatment or substance designed to have no therapeutic effect).

Having a control group is one of the cornerstones of modern clinical trials.

An example of a control group in COVID-19 research is this recent study . People with diabetes hospitalised for COVID-19 were divided into those receiving the drug metformin and those not receiving it (the control group).

Back to ginseng. Today, it is a popular herbal remedy. As to whether it improves stamina, a recent review found some evidence ginseng might help men with erectile dysfunction .

Rhubarb in 18th-century England

Rhubarb roots have been used as a laxative for more than 5,000 years , including in 18th-century England.

Caleb Parry , an English physician working in Bath, wanted to know whether locally grown rhubarb was as good as the more expensive Turkish variety.

In 1786, he ran a study in which he switched the type of rhubarb he gave to each patient at different times. He then compared each patient’s symptoms while eating each type of rhubarb. He concluded there was no advantage in using the Turkish version.

This is the first published example of a crossover trial (a study where the participants receive each treatment at different times).

Today, we know rhubarb roots and stems are rich in anthraquinones , which have a laxative effect .

Early 20th-century randomised trial

Beriberi , a disease that can have lasting effects on the nervous system and heart, was common in Southeast Asia in the early part of the 20th century.

In 1905 a beriberi outbreak occured at the Kuala Lumpur Lunatic Asylum. At that time William Fletcher was the district surgeon. He realised the outbreak provided an excellent opportunity to run an experiment (which we now know is just a bit unethical).

Read more: Looking back on the chequered past of drug trials

Each patient was assigned a number. Those with even numbers were sent to one ward and given brown unpolished rice to eat. Those with odd numbers went to another ward and given white polished rice.

At the end of the experiment, 15% of the patients who ate the white rice died of beriberi; none given brown rice died.

Read more: Health Check: can vitamins supplement a poor diet?

This is a very early example of randomisation in a clinical trial, where one group is chosen at random to receive a treatment.

Randomisation is another very important factor in good clinical trial design.

Today we know beriberi is caused by a deficiency in thiamine (vitamin B1) and a white rice diet is deficient in thiamine .

Tuberculosis and the randomised controlled trial

Sir Austin Bradford Hill , an English epidemiologist and statistician, conducted the first randomised controlled trial in 1948. The trial was to treat the lung disease tuberculosis.

Bradford Hill decided whether a patient should be treated with the antibiotic streptomycin plus bed rest, or bed rest alone, by using a table of random numbers.

The investigators didn’t know which patient got each treatment; details were in sealed envelopes. Patients were not told they were in a trial.

Using sealed envelopes is an example of what we now call allocation concealment . Making sure neither investigators nor patients know which treatment they are receiving is called blinding . These are now standard features of randomised controlled trials.

Randomised controlled trials are the “gold standard” of clinical trial designs, due to the use of both a control group and randomisation.

Decades later, researchers have used a randomised controlled trial to test the drug ruxolitinib in patients with severe COVID-19.

So, although Bradford Hill conducted the first randomised controlled trial, it was based on hundreds of years of people working out why things like a control group and randomisation are so important.

Read more: Randomised control trials: what makes them the gold standard in medical research?

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For more than a century, NIH scientists and supported scientists have paved the way for important discoveries that improve health and save lives. In fact, 171 scientists who won the  Nobel Prize  conducted their work at NIH or were supported by NIH funds. Their studies have led to the development of MRI, understanding of how viruses can cause cancer, insights into cholesterol control, and knowledge of how our brain processes visual information, among dozens of other advances.

The Roots of NIH

The National Institutes of Health traces its roots to 1887, when a one-room laboratory was created within the Marine Hospital Service (MHS), predecessor agency to the U.S. Public Health Service (PHS).

The MHS had been established in 1798 to provide for the medical care of merchant seamen. In the 1880s, the MHS had been charged by Congress with examining passengers on arriving ships for clinical signs of infectious diseases, especially for the dreaded diseases cholera and yellow fever, in order to prevent epidemics. Read A Short History of NIH .

Chronology of Events

Significant events and major research advances in NIH history.

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Federal legislation that had a major influence on the growth of the NIH, from its beginning as the Marine Hospital Service in 1798.

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NIH is responsive to Congressional legislation that adjusts NIH's programs to meet changing research needs. As a result of the NIH reauthorization process, NIH is able to respond strategically in an era when medical research requires constant innovation and increased interdisciplinary efforts.

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We are committed to exploring the history of medicine in its broadest sense, both geographically and chronologically; we offer a range of graduate and undergraduate courses on topics such as the History of Chinese Medicine; Colonial Knowledge; Health and Healing in Early Modern England; Darwin, Freud, and Pasteur; and Disease Control in Historical Perspective.

The Institute of the History of Medicine is located in the William H. Welch Medical Library, named after the first Chair of the Department of the History of Medicine. In establishing the first Department of the History of Medicine in the English-speaking world, Welch sought to provide a humanistic component to medical education and public health.

The Department of the History of Medicine trains PhDs in the history of medicine, and teaches in the schools of medicine and public health. We offer courses for undergraduates and graduate students on the main arts and sciences campus of Johns Hopkins University.

The departmental library of the Institute, the Historical Collection is also the resource center for the history of medicine for the Hopkins community, and hosts visiting scholars from the United States and abroad. A research collection covering all aspects of the history of medicine, public health and allied sciences, it contains over 70,000 volumes. A large, comprehensive library of secondary sources accompanies a smaller, but choice collection of rare books, manuscripts, prints, photographs, medals, stamps and objects.

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The Department of the History of Medicine at Johns Hopkins, the first department of its type in North America, is proud to introduce new online CME modules that provide a historical perspective on issues of relevance to clinical practice today.

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The Office of NIH History and Stetten Museum

Our mission statement:  nih history lives here.

The Office of NIH History and Stetten Museum (ONHM) at the National Institutes of Health (NIH) advances the historical understanding of the biomedical research conducted at the NIH by documenting, preserving, and interpreting the history of significant NIH achievements, scientists, and policies.  Among other activities, the office creates innovative exhibits and helps scholars and researchers to navigate the rich history of the NIH.

Be a part of history by donating your scientific materials or volunteering in the office.  Send us a message at  [email protected] .    

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Learn more about what we've been working on recently as well as more about ONHM in our 2022 Annual Report . (15 MB)

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In the early 1950s, the NIH got an unofficial agency "historian" when Louise Endicott, a staff member of the NIH Scientific Reports Branch, asked to be appointed as one. She served in that capacity until her retirement in 1956. In 1962, Dr. Wyndham Miles became the first professional historian for the NIH. He served until 1974, when he moved to the History of Medicine Division at the NIH National Library of Medicine.

In planning for the commemoration of the NIH's centennial in 1987, Dr. DeWitt Stetten Jr., proposed the establishment of a museum of medical research to preserve the material heritage of the NIH. Stetten had first come to the NIH in 1954 as director of the intramural research program of the National Institute of Arthritis and Metabolic Diseases. He left in 1962 to become the first dean of the Rutgers Medical School, but returned to the NIH in 1970 as director of the National Institute of General Medical Sciences. Stetten then became the senior scientific adviser to the NIH director from 1979-1986. R eviving the position of historian was a component of his proposed museum and in 1986, Dr. Victoria A. Harden was appointed NIH Historian and Curator. In May 1987, the museum was renamed in honor of Stetten.

Harden retired in 2006, and Dr. Robert Martensen served as the next director from 2007 until 2012. The office continued under acting directors until Kim Pelis, Ph.D. was appointed in March 2022.

Dedication of the DeWitt Stetten, Jr., Museum of Medical Research. Stetten had proposed the creation of a museum of medical instruments at the NIH in the early 1980s.

What the Office of NIH History and Stetten Museum Does

The NIH biomedical and behavioral research community is an extraordinarily dynamic one, with a flow of talent from across the globe that relies on, and sometimes invents, ever-changing technologies. The Office of NIH History and Stetten Museum (ONHM) helps to preserve the memory of who worked with whom, of when the work was conducted, and of what tools they used. NIH scientists and others in the community can legally transfer historical properties to ONHM, where they can become part of our national legacy. The material heritage in our collections includes instruments, objects, images, films, voices, notebooks, illustrations, and myriad documents. These historical resources preserved by ONHM are of inestimable value to this and future generations of researchers. Some less-formal correspondence covering topics — such as policy, finance, public relations, hiring strategies of lab personnel, as well as milestone discoveries — can illuminate perspectives not captured in formal journal publications.

We welcome the opportunity to evaluate materials or objects for donation. Due to space constraints, we're selective in the types and formats of donations. However, if prospective donations fall outside the scope of our collection focus, we will gladly advise you on suitable repositories for your materials among our colleagues. Please see our "Get Involved" section of this website.

Search our collections online or contact the NIH Stetten Museum curator .

Where We Fit In With Other History-of-Medicine Repositories

ONHM focuses its research and collecting specifically on the NIH research community — its people, its facilities and resources, its contributions to national and international research initiatives, and its interactions with scientific educational institutions and technology innovation centers. It is that focus on NIH that separates the office from the NIH National Library of Medicine (NLM), which has a broad mandate to cover medical history from all times and from all places over the globe. ONHM works closely with NLM to be sure that important historical resources are saved and made accessible.

The Office of NIH History is a component of the Office of Intramural Research (OIR) in the Office of the Director.  OIR is responsible for the budget and operational authority of the Office of NIH History.  In planning for the work of the office, the Deputy Director of Intramural Research and the Director of the Office of NIH History are advised by a committee of federal scientists and historians.

Advisory Committee

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Bulletin of the History of Medicine

Jeremy A. Greene, M.D., Ph.D., Johns Hopkins University; Alisha Rankin, Ph.D., Tufts University; Gabriela Soto Laveaga, Ph.D., Harvard University

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The  Bulletin  publishes scholarly articles spanning the social, cultural, and scientific aspects of the history of medicine worldwide. Articles are based on historical research in primary sources grounded in the robust secondary literature in the history of medicine. Article submissions should clearly make critical interpretations and place the story in historical context. The  Bulletin  subscribes to the principles of the Committee on Publication Ethics ( COPE ). The  Bulletin  does not publish material that is available elsewhere, in any language, at the time of its publication in the journal, or material for which we must acknowledge permission to another publisher. We regularly publish articles that later appear as chapters in books, but the journal and its publisher, The Johns Hopkins University Press, hold the copyright, and the book publisher must request permission to reprint. Publication of the journal article  must  antedate publication of the book.

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The Wellcome Trust  has changed its access policy concerning research articles that have been funded by the Trust. The new policy which became effective on January 1, 2021 can be found at:  wellcome.org/grant-funding/guidance/open-access-guidance/open-access-policy .

Authors employed by NIH : The publisher and editors of the Bulletin understand that authors employed by NIH are obligated to post their articles in PMC. The Bulletin uses a two-step process in order to ensure that our authors can comply with this mandate. As is the case for most federal employee authors, the Bulletin cannot hold copyright of the article.

• Upon acceptance of an article by the Bulletin, the author should post a copy of the final manuscript of the essay to PMC specifying a 12-month embargo. The essay must be accompanied by the  NIH publishing agreement and manuscript cover sheet  and the Johns Hopkins University Press publishing agreement for U.S. Government employees, available from the editorial office. Copies of these documents should also be forwarded to the editorial office.  Please note : the manuscript version of the essay is to be submitted to PMC in order for our authors to be in compliance, but the final PDF of the published essay is the version that will ultimately circulate on PMC.
• The BHM editorial office will take responsibility for posting the PDF of the final, published version of the article by the end of the 12-month embargo period, whereupon PMC will ignore the previously submitted unedited manuscript version of the essay. PMC will contact the author to confirm his or her permission to post the PDF version.

Preparing Your Manuscript for Submission

• BHM  manuscripts should not exceed 12,000 words (including endnotes). Manuscripts over the word limit will not be considered.
• Double-space your manuscript: text, notes, and quotations.
• Use the same type size and font for all material.
• Quotations of more than six typed lines should be indented from the left margin and typed in a block format (double-spaced).
• Use American spelling.
• Dates should be written as, for example, “June 7, 2010.”
• To answer general questions about style and usage in  BHM,  refer to the  Chicago Manual of Style  (15th edition;  CMS ).
• BHM  requires numbered endnotes without a bibliography (See the  CMS , 16th ed.).
• Document fully .  BHM  prefers to identify the source of each separate quotation with its own note; please do not bundle citations into a single note at the end of the paragraph. Please note that  BHM  requires inclusive page numbers for book chapters in edited collections and for all journal articles, in addition to page numbers for direct quotations.  BHM  uses  abbreviations for journal names .   For books and journals, follow the  Bulletin  examples given below; for more complex references, follow  CMS .

Remember to provide: Full first names and middle initial(s) for authors and editors Subtitles of books and articles Full names of foreign journals cited Name of the publisher for books published after 1900 For newspaper articles, the author, title of article, and page numbers if available. Exact and inclusive page numbers for all quotations

The second and succeeding citations of references should refer back to the first full citation.

Citation Examples

1. Michael Worboys,  Spreading Germs: Disease Theories and Medical Practice in Britain, 1865–1900  (Cambridge: Cambridge University Press, 2000), 81. [Book with page number for direct quotation]

2. Stephen Palmer, “Central American Encounters with Rockefeller Public Health, 1914–1921,” in  Close Encounters of Empire: Writing the Cultural history of U.S.–Latin American Relations,  ed. Catherine LeGrand, Gilbert Joseph, and Ricardo Salvatore (Durham, N.C.: Duke University Press, 1999), 311 – 32, quotation on 320. [Chapter in edited book with inclusive page numbers and page number for direct quotation]

3. Alexandra Stern, “Making Better Babies: Public Health and Race Betterment in Indiana, 1920–1935,”  Amer. J. Public Health  90 (2002): 742 – 52, quotation on 751. [Journal article with inclusive page nos. and page no. of direct quotation.]

4. Ibid., 750.

5. Palmer, “Central American Encounters” (n. 2), 312. [Short form for previously cited item]

6. James Smith, “Public Health Experiments,” in LeGrand, Joseph, and Salvatore,  Close Encounters  (n. 2), 100 – 134. [Chapter in previously cited book]

7. Lauren Nauta, “Medical Development in New Jersey” (Ph.D. diss., University of Pennsylvania, 2006). [Dissertation]

Illustrations

• Illustrations are printed in black and white only. Photographs may be sent to the editorial office as glossy black-and-white 5” x 7” prints (do not send photos in color), or uploaded in TIFF or EPS formats. Halftones (art with any shades of grey) should be 266–300 dpi; line art, 900–1200 dpi. Do not use Word, PDF, or GIF files for illustrations.
• Indicate the approximate placement of all illustrations in the text. Provide captions for all tables and figures. Captions should include credit to the original sources.

Permissions

• You will need to provide copies of letters granting permission to reprint illustrations.

Unpublished theses present a particular problem. If you are quoting more than five sentences from such an unpublished work, please provide a letter granting permission from the author of the thesis or from the sponsoring university.

Uploading Your Manuscript

• At this time, ScholarOne cannot upload Word 2007 documents. Please save your manuscript as a Word 1997–2003 document before submitting.
• Please make sure to remove any identifying information (name, university, etc.) from the manuscript itself, as  BHM  reviews are double blind. Author acknowledgments are useful for the editorial office, but can also reveal the author’s institution or identity; therefore, please upload your acknowledgments in a separate file, selecting the file designation “Title Page” on ScholarOne Manuscripts.
• Please supply a required summary of 150 or fewer words with your paper.
• Please provide the required 4 to 8 keywords for indexing purposes.

The Hopkins Press Journals Ethics and Malpractice Statement can be found at the ethics-and-malpractice  page.

Peer Review Policy

The  Bulletin of the History of Medicine  publishes scholarly articles spanning the social, cultural, and scientific aspects of the history of medicine worldwide. Articles are based on historical research in primary sources grounded in the robust secondary literature in the history of medicine. The  Bulletin  does not publish material that is available elsewhere, in any language, at the time of its publication in the journal, or material for which we must acknowledge permission to another publisher. We regularly publish articles that later appear as chapters in books, but the journal and its publisher, The Johns Hopkins University Press, hold the copyright, and the book publisher must request permission to reprint. Publication of the journal article must antedate publication of the book. In addition to offering new information based on scholarly historical research in primary sources, authors are expected to make critical interpretations and to place their narratives in a suitable historical and historiographic context. Authors must explain how their contribution fits in with the existing history of medicine literature on the topic at hand.  Each article submitted for publication is assessed first by the editors (to make sure that it is a research article, and not a general overview of a subject), and then, if the article seems appropriate for the journal, by 3-4 historians expert in the particular area covered by the article. The journal utilizes a strict double-blind review process. If revisions are requested, the editors will decide which or all of the original reviewers to send the revised paper for re-reviewing. The approximate time between submission to initial decision is 4 months.

Dear xxxxxxx:

I am writing to request permission to reprint the illustration titled "xxxx." It appeared on page xx of (book or journal title), edited by xxxxx, in (year).This illustration is to appear as originally published [or with changes or deletions as noted] in "YOUR ARTICLE'S TITLE," by AUTHOR'S NAME, which the Johns Hopkins University Press is currently preparing for publication. This article is scheduled to be published in the MONTH, YEAR, issue of the Bulletin of the History of Medicine, in a press run of about 1,750 copies.I am requesting nonexclusive world rights to use this illustration in this journal in all languages and for all editions, in print and online, in all retrieval systems now or ever invented. Full acknowledgment will be given in the journal. Please sign below and return one copy of this letter to me to indicate your consent.If you don't hold the copyright to xxxx, or if I must seek permission from another source, please note the fact below. Should it be necessary for me to seek permission elsewhere, any information you could provide to help me contact the proper party would be greatly appreciated.Thank you for your consideration of this request.

The above request for permission to reprint is approved on the conditions specified below and on the understanding that full credit will be given to the source. The acknowledgment should read as follows:

Approved by:

Announcement from the Publisher

We have recently expanded the rights granted to contributors in our standard permissions agreement by allowing authors to include their articles in institutional depositories. Previously, the Press had restricted use to personal or departmental databases or on-line sites.

The change recognizes the important role institutions play in the scholarly communication process. It seems reasonable that the scholarship produced by faculty members should be made available to others within that same institution which, after all/ is providing either direct or indirect support.

The full text of the section that outlines author's rights is reproduced below. The new language is in point 4.

Rights of the Author: You have the following nonexclusive rights: (1) to use the Article in your own teaching activities; (2) to publish the Article, or permit its publication, as a part of any book you may write; (3) to include the Article in your own personal or departmental database or on-line site;  (4) to include the article in your institutional database provided the database does not directly compete with either the Johns Hopkins University Press or Project Muse,is non-commercial, is institution-specific and not a repository that is discipline-based and/or accepts contributions from outside the institution. For use (4), you agree to request prior permission from the Press.

For all rights granted in this paragraph, you agree to credit the Press as publisher and copyright holder.

Special Issues

The Editors welcome proposals for special issues of the Bulletin that address themes of interest to the Bulletin 's wide-ranging readership—themes that go beyond a narrow time or place or topic and offer a rich array of perspectives and ideas. Successful special issues include a substantial introduction, written by the Guest Editor(s), that orients readers to the significance of the topic and situates the essays in the volume in a broad historiography of medicine, health, and healing.

A special issue can accommodate up to 10 articles, each with a maximum of 12,000 words of text (including footnotes). All special issue manuscripts are treated with the same protocol as regular Bulletin submissions: all manuscripts are sent out for peer review, where each essay is assessed by three external reviewers. (Please note: peer review will not commence until all special issue essays have been submitted.) The Editors make the final decision about publication but will consult with the Guest Editors as needed. A draft of the introduction may be reviewed by the Editors or sent to one or two scholars for comments and suggestions for improvement. Special issues are usually published approximately 12–18 months after manuscripts have been received. If, after peer review, only a few articles are accepted (~3), they can be published as a special section within a regular issue of the journal and include an abbreviated introduction. If fewer than three articles are accepted, they can be published in the Bulletin as regular articles.

Proposals should include : the names of the Guest Editor(s), a description and rationale for the issue (2-3 pp.), and a list of potential contributors and the titles and abstracts of their articles. The description and rationale should explain why the topic is (or should be) of interest to a broad array of historians of medicine. Proposals should be sent to the Editors at [email protected] .

Special Sections

A Bulletin special section can accommodate 3 articles, each with a maximum of 12,000 words of text (including footnotes), organized around a theme of interest to the journal’s readership. A short introduction should introduce the special section. All manuscripts are treated with the same protocol as regular Bulletin submissions: all manuscripts are sent out for peer review, where each essay is assessed by three external reviewers (peer review will not commence until all essays have been submitted). The Editors make the final decision about publication but will consult with the Guest Editors as needed. A draft of the introduction may be reviewed by the Editors or sent to one or two scholars for comments and suggestions for improvement. If fewer than three articles are accepted, they can be published in the Bulletin as regular articles.

Proposals should include : the names of the Guest Editor(s), a description and rationale for the section, and a list of potential contributors and the titles and abstracts of their articles. The description and rationale should explain why the topic is (or should be) of interest to a broad array of historians of medicine. Proposals should be sent to the Editors at [email protected] .

Forums can accommodate approximately 4-6 shorter essays (3,000-4,000 words, including footnotes). A Forum should be organized around a recognizable and coherent theme for which a Guest Editor should take responsibility (including an open call, if applicable). This flexible format allows for academic perspectives but also first-person perspectives. A short introduction should introduce the Forum. Forum pieces will not be subject to external peer review, but will be assessed by the Editors/Editorial Board. Editors make the final decision about publication but will consult with the Guest Editor(s) as needed. A draft of the introduction may be reviewed by the Editors or sent to one or two scholars for comments and suggestions for improvement.

Proposals should include : the names of the Guest Editor(s), a description and rationale for the Forum, and a list of potential contributors and essays. The description and rationale should explain why the topic is (or should be) of interest to a broad array of historians of medicine. Proposals should be sent to the editors at [email protected] .

Jeremy A. Greene  Alisha Rankin  Gabriela Soto Laveaga

Associate Editor

Carolyn McLaughlin

Advisory Editorial Board

Jacqueline Antonovich Catherine Burns Lara Freidenfelds Joseph Gabriel Lisa Haushofer Anne Kviem Lie Raúl Necochea López Abena Dove Osseo-Asare Deirdre Cooper Owens Akihito Suzuki Carsten Timmermann Dora Vargha

AAHM Officers

Barron Lerner, President Mary Fissell, Vice President Sarah Handley-Cousins, Secretary Scott Podolsky, Treasurer Keith Wailoo, Immediate Past President

AAHM Councilors

Adam Biggs  Mary Augusta Brazelton  Pablo F. Gómez  Rana Hogarth  Rebecca Kluchin  Jessica Martucci  Wangui Muigai  Projit Bihari Mukharji  Kelly O’Donnell  Kavita Sivaramakrishnan  Jacob Steere-Williams  Harry Yi-Jui-Wu

The Bulletin of the History of Medicine regularly publishes reviews of a broad range of scholarly books in our field. The Editors carefully select potential reviewers whose scholarship allows for informed critiques of the books under review; this includes established historians and advanced PhD-level graduate students. The Editors also welcome proposals for book reviews, but the journal will not consider submissions of completed reviews prior to assignment. Reviewers will be asked to declare any potential conflicts of interest. Please contact the editorial office at [email protected] to discuss book review proposals. 

Should you wish to be considered for a book review in the future, we would be happy to add you to our reviewer database. Please email a CV to the editorial office for consideration.

If you would like the Bulletin to review your book, please request that your publisher send a review copy to the editorial office below. Review copies received by the Johns Hopkins University Press office will be discarded.

The Editors Bulletin of the History of Medicine Johns Hopkins Institute of the History of Medicine 1900 East Monument Street, Rm. 306 Baltimore, MD 21205

Abstracting & Indexing Databases

• Bibliography of Asian Studies (Online), 1971-1992
• Arts & Humanities Citation Index
• Biological Abstracts (Online)
• BIOSIS Previews
• Current Contents
• Web of Science
• British and Irish Archaeological Bibliography (Online)
• Dietrich's Index Philosophicus
• IBZ - Internationale Bibliographie der Geistes- und Sozialwissenschaftlichen Zeitschriftenliteratur
• Internationale Bibliographie der Rezensionen Geistes- und Sozialwissenschaftlicher Literatur
• Academic Search Alumni Edition, 1/1/2003-
• Academic Search Complete, 1/1/2003-
• Academic Search Elite, 1/1/2003-
• Academic Search Premier, 1/1/2003-
• America: History and Life, 1/1/1963-
• Biography Index: Past and Present (H.W. Wilson), vol.76, no.1, 2002-vol.84, no.3, 2010
• Book Review Digest Plus (H.W. Wilson), Mar.2002-
• Current Abstracts, 1/1/2003-
• Gender Studies Database, 1/1/1940-
• General Science Abstracts (H.W. Wilson), 2002/03-
• General Science Full Text (H.W. Wilson), 4/15/2002-
• Historical Abstracts (Online), 1/1/1963-
• Library & Information Science Source, 4/1/1938-5/1/1939
• MLA International Bibliography (Modern Language Association)
• OmniFile Full Text Mega (H.W. Wilson), 4/15/2002-
• Public Affairs Index, 9/1/2003-
• Russian Academy of Sciences Bibliographies
• SocINDEX, 7/1/1973-
• SocINDEX with Full Text, 7/1/1973-
• STM Source, 9/1/2006-
• TOC Premier (Table of Contents), 1/1/2003-
• Women's Studies International, 1/1/1940-
• Scopus, 1945-ongoing
• Academic ASAP, 03/2002-06/2017
• Book Review Index Plus
• Gale Academic OneFile
• Gale Academic OneFile Select, 03/2002-
• Gale General OneFile, 03/2002-
• InfoTrac Custom, 3/2002-
• General Science Index, 2002/03-
• ArticleFirst, vol.64, no.1, 1990-vol.85, no.2, 2011
• Electronic Collections Online, vol.70, no.1, 1996-vol.85, no.2, 2011
• Personal Alert (E-mail)
• Health & Medical Collection, 4/1/2003-
• Health Research Premium Collection, 4/1/2003-
• Hospital Premium Collection, 4/1/2003-
• Medical Database, 4/1/2003-
• Periodicals Index Online
• Professional ProQuest Central, 04/01/2003-
• ProQuest 5000, 04/01/2003-
• ProQuest 5000 International, 04/01/2003-
• ProQuest Central, 04/01/2003-
• Research Library, 04/01/2003-
• Science Database, 04/01/2003-
• SciTech Premium Collection
• Abstracts in Anthropology (Online)

Abstracting & Indexing Sources

• Abstracts of Mycology   (Ceased)  (Print)
• Chemical Abstracts (Print)   (Ceased)  (Print)
• Index Medicus   (Ceased)  (Print)
• Index to Scientific Reviews   (Ceased)  (Print)
• Inpharma Weekly   (Ceased)  (Print)
• International Nursing Index   (Ceased)  (Print)
• Numismatic Literature   (Ceased)  (Print)

Source: Ulrichsweb Global Serials Directory.

1.0  (2022) 1.2 (Five-Year Impact Factor) 0.00102 (Eigenfactor™ Score) Rank in Category (by Journal Impact Factor): 22 of 48 journals, in “History & Philosophy of Science”

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Readers include: Historians, physicians, nurses, archivists, curators, librarians, and others interested in the social, cultural, and scientific aspects of the history of medicine worldwide

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American Association for the History of Medicine

The Institute of the History of Medicine at Johns Hopkins University

The Bulletin’s online syllabus archive in the history of medicine

A blog about teaching the history of medicine.

The  Bulletin   provides blog posts on teaching resources in the history of medicine and maintains a syllabus archive at the blog,  Recommended Dose  at  teachhistmed.com

eTOC (Electronic Table of Contents) alerts can be delivered to your inbox when this or any Hopkins Press journal is published via your ProjectMUSE MyMUSE account. Visit the eTOC instructions page for detailed instructions on setting up your MyMUSE account and alerts.  

Also of Interest

Martha Montello, Harvard Medical School

Christopher Sogge, Johns Hopkins University

Anne Reinhardt, Williams College

Deborah Boyle, College of Charleston

Sandie Holguín and Jennifer Davis, University of Oklahoma

Georgia L. Irby, College of William & Mary

Clare Anderson, University of Leicester

Robin Mitchell-Boyask, Temple University

Rachel S. Harris, Florida Atlantic University

Melissa A. Wong, University of Illinois Urbana-Champaign

Steven Miles, Hong Kong University of Science and Technology

Bruce Holsinger, University of Virginia

Dr. Nora Gilbert, University of North Texas

Ellysa Stern Cahoy, Penn State University

Michael Blackie, University of Illinois at Chicago

Werdie van Staden, University of Pretoria, South Africa

Nicholas Morrow Williams, Arizona State University

James M. DuBois, Washington University in St. Louis; Ana S. Iltis, Wake Forest University; and  Heidi Walsh, Washington University in St. Louis

Virginia M. Brennan, PhD, MA, Meharry Medical College

Ruth Oldenziel, Eindhoven University of Technology

Jane Hanenberg, Boston Psychoanalytic Society and Institute

Ari Kelman, University of California, Davis

Judah M. Cohen, Indiana University; Jessica Cooperman, Muhlenberg College; and  Marni Davis, Georgia State University

 A. Hal Strelnick, MD; Albert Einstein College of Medicine and Karen Calhoun, MA University of Michigan Institute for Clinical & Health Research

Leona Toker, The Hebrew University of Jerusalem

Sabine Huebner, University of Basel, Switzerland

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INFORMATION FOR

• Residents & Fellows
• Researchers

Locating Medical History: The Stories and Their Meanings

• Features Lessons from the depths Leaving no child behind In the anatomy lab, a new way of thinking From scarcity to plenty since Colonial days What’s in a name? Yale team provides tsunami relief
• News Yale and New Haven join in pilot program for treating HIV/AIDS in Russia The molecule meets the computer chip New MBA program to help health professions with the business of medicine A new hospital pavilion, set to open in 2008, will house $430 million cancer facility Kaplan named to IOM Yale joins national epilepsy study A new role for “good” microorganisms Minority patients wait longer for angioplasty Camera in a pill gives an unprecedented view of the small intestine Lipid found to play key role in transmitting information between synapses Gambling and elder health Growth factor linked to asthma Melanin can be good, or bad Alzheimer’s protein solved Compleat Pediatricians disbanded Fall 1980 May 1955
• People The eternal triangle of a sound health system Gordon receives the Peter Parker Medal for years of service In Memoriam Noted chemist is named provost New finance officer crosses country to assume post at Yale Three named to endowed professorships Charles H. Cha, M.D. Susan E. Hardy, M.D., FW Gilbert H. Glaser, M.D., Sc.D. Roberto J. Groszmann, M.D. Marina R. Picciotto, Ph.D. John A. Persing, M.D Sally E. Shaywitz, M.D. Charles M. Radding, M.D. Albert C. Lo, M.D., Ph.D., HS Judith H. Lichtman, Ph.D., M.P.H. Janet B. Henrich, M.D. Teresa A. Ponn, M.D. Susan T. Mayne, Ph.D. Hunting the secrets of the cell in San Francisco, and game fish across the globe Turning the tide of AIDS in New Haven, in a collaborative style Three med school alumni elected to Institute of Medicine 1950s - Hindle Barry L. Zaret, M.D. Stephen G. Waxman, Ph.D., M.D. Robert S. Sherwin, M.D., FW Heping Zhang, Ph.D. 1960s - Jonas 1970s - Silken 2000s - Oen-Hsiao 1970s - Greene 2000s - Ahmad 1970s - Krause 1990s - Klenoff 1970s - Rodin 1980s - Fish 2000s - Leaderer Good doctors and great doctors Former surgeon general urges PA graduates to “look for a calling” Auction raises more than $26,000 Downs fellows share a world of knowledge Gary, Lisa / Quon, Nicole C. / Snyder, Angela B. Mesesan, Kyeen 2000s - Wong Bindra, Ranjit 2000s - Paulson
• Dialogue Fish tales, on the up and up The 80-hour week, and its aftermath Enlightened HMOs make time for CME Merson steps down as dean of public health Flashcards for the boards How to save the life of a young driver A Doctor’s Visit: Three Novellas & Five Short Stories From Neuroscience to Neurology: Neuroscience, Molecular Medicine, and the Therapeutic Transformation of Neurology A Woman’s Guide to Menopause & Perimenopause Academia to Biotechnology: Career Changes at Any Stage Viral Encephalitis in Humans Principles and Practice of Radiation Oncology, 4th ed. The Optimist: Meditations on Medicine Invisible Cities: A Metaphorical Complex Adaptive System Cut Your Cholesterol: Featuring the Exclusive Live It Down Plan Acid Related Diseases: Biology and Treatment, 2nd ed. Clinical Nuclear Cardiology: State of the Art and Future Directions, 3rd ed. Library keeps a watchful eye on what works on the Web A molecular link between the brain and learning What role for the states in stem cell research? Healthcare for Children on the Autism Spectrum: A Guide to Medical, Nutritional, and Behavioral Issues Cancer: Principles & Practice of Oncology, 7th ed. Locating Medical History: The Stories and Their Meanings Experiences of Depression: Theoretical, Clinical, and Research Perspectives Field Guide to Internal Medicine Group Psychotherapy and Recovery From Addiction: Carrying the Message Using revolutionary technology to find “a rusty old ship” A moral argument for fighting diseases of the poor Second opinion

edited by Frank Huisman and John Harley Warner, Ph.D., professor and chair of the Section of the History of Medicine (Johns Hopkins University Press) At a time when the study of medical history is facing choices about its future, these scholars explore the discipline’s distant and recent past in order to rethink its missions and methods today.

Featured in this article

• John Warner, PhD Avalon Professor in the History of Medicine and Professor of American Studies and of History; Chair (on leave Spring 2024), History of Medicine

• Explore Stanford Medicine's Past
• Research Medical History
• Online Exhibits
• Events & News
• Historic Images     
• Bassett Anatomy

Research History of Medicine Topics

• Change the drop-down menu from "Keyword" to "Publisher"
• Enter the word "university" in the search box
• Retrieved search results will have the word "university" in their company name, such as Stanford University Press and Oxford University Press
• Click on the link "Libraries Worldwide" to see if a particular book is at Stanford
• Search Searchworks
• If an item is not at Stanford, request via DocXpress Document Delivery Service

Dissertations

PhD dissertations sometimes include a literature review on their topic. These reviews can be helpful in building your bibliography.

• Search by topic in ProQuest

To further build a bibliography, you can post an inquiry or review past postings on the following listservs:

• H-Sci-Med-Tech
• Bulletin of the History of Medicine (Johns Hopkins University)
• Journal of the History of Medicine and Allied Sciences (Oxford University)
• Limit to "Historical Article" from the "Type of Article"

Once you have generated a list of books and articles, you can further build your bibliography by investigating where those books and articles have been cited in other books and articles.

• Search for citations in Web of Science

Reference Works

Reference works can provide a broad overview of a topic, and also furnish a starting bibliography. We carry encyclopedias, chronologies, necrologies, and other standard reference sources in the history of medicine, including:

• Companion Encyclopedia of the History of Medicine by W.F. Bynum and Roy Porter (New York: Routledge, 1993).
• Encyclopedia of Medical History by Robert E. McGrew (New York: Macmillan Press, 1985).
• A Chronology of Medicine and Related Sciences by Leslie T. Morton and Robert J. Moore (Brookfield, Vermont: Ashgate Publishing, 1997).
• The Greatest Benefit to Mankind by Roy Porter (New York: W.W.Norton & Company, 1997).

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Q&A: Medical historians examine organization's silence over rise of Nazism

by Alvin Powell, Harvard Gazette

In December, the New England Journal of Medicine began a process of self-examination, publishing articles about the journal itself and its handling of a series of key historical injustices in medicine, including eugenics, slavery, oppression of Native Americans, and, in an issue published in April, the rise of Nazi Germany.

One major challenge, according to two medical historians, is how little the NEJM had to say about Nazism and its systematic and genocidal oppression of Europe's Jews beginning in 1933, when Adolf Hitler came to power.

That came as something of a surprise to Allan Brandt, the Amalie Moses Kass Professor of the History of Medicine and professor of the history of science, and Joelle Abi-Rached, Ph.D. '17, the Mildred Londa Weisman Fellow at the Radcliffe Institute for Advanced Study.

The pair contributed to the series, which was initiated by David Jones, the A. Bernard Ackerman Professor of the Culture of Medicine. Brandt praised the publication for its willingness to face what may be an uncomfortable history.

With so little material available, the two researchers, in a conversation with the Gazette, discussed their dilemma: How do you parse a near silence? This interview was edited for length and clarity.

Knowing the prevailing attitudes toward race and ethnicity in the World War II era and the decade leading up to it, were you expecting to find a complicated situation?

Brandt: Yes. The New England Journal 's effort is very similar to what Harvard did in its exploration of slavery on campus—Harvard faculty and administrators held slaves and did not challenge slavery often. These are the kinds of institutional self-observations that I think are important. It's often been perceived as a reputational risk in opening up the archives and facing these things. But I think the reputational risk is in not doing it and NEJM very appropriately recognized that.

When we look at your specific findings, what do you think is most important?

Brandt: When our colleagues were working on other papers in this series and ran their digital investigations, they literally came up with hundreds of hits. For us, the experience was like putting a search term into Google and getting no response. We expected that, given the dimensions and the horrors of the Holocaust, we would find that NEJM said a lot during that time. But our initial finding was that there was almost nothing.

Abi-Rached: The omission, absence, and silence startled us, so we made an extra effort to find anything that was written on the rise of Hitler. We did come across a few items and these became the backbone of the paper. They were illuminating.

One piece published in 1933 is a very short piece that even people who have read our paper have trouble finding. It's a short communique published at the end of a very long and tedious paper on surgery. The communique, "The Abuse of the Jewish Physicians," is revealing because the concern was not discrimination or persecution but the fact that these Jewish physicians were dismissed and lost their livelihood. That was the only piece published in 1933.

Then there is a controversial, longer piece published in 1935 by Michael Davis, an eminent health reformer, with a German nurse who later research would reveal was a Nazi sympathizer. And then there was nothing until 1944.

In 1944, NEJM published its first editorial, an important piece in which the journal takes a stance on the humanitarian disaster that the "Nazi tyranny" had caused in occupied Europe.

Then you have another key article published in 1949, long after the conclusion of the Second World War, by Leo Alexander, who was a Viennese-born neuropsychiatrist who gathered evidence for the trial of the doctors at Nuremberg. So, this absence of debate around the rise of Nazism and its persecutory, racist laws became our guiding thread.

How would you describe what must have been the journal's approach during those years?

Brandt: Joelle and I talked about how we could understand silence, or an omission. We speculated about structural or institutional racism and thought about whether, in a medical or scientific journal which is typically reporting clinical findings and new knowledge, it might have been possible for editors to say, "This isn't really part of our remit. It's terrible, but that's not what we do."

So we decided to go to other leading journals, Science and the Journal of the American Medical Association , to see if that held up—sometimes you have to go outside to look on the inside. We couldn't get to it in an article of this length, but I think if we more closely examined Boston medicine at the time, between academics at Harvard Medical School and NEJM , we might have gotten additional insight. It was not a diverse group.

Abi-Rached: The point we make is that the silence, the omission, was not banal. It was not mere ignorance. The discriminatory nature of these policies that were implemented by the Nazi regime were reported in the U.S. press.

JAMA and Science did report on what was happening in Germany vis-à-vis the Jewish physicians, who were the victims of such policies. The Dachau concentration camp was established in 1933 and Davis and Krueger, for example, mentioned labor camps in their piece, but they omitted the term "forced" labor camps, rendering them somehow unproblematic.

These camps were mentioned in other journals, the persecution of Jewish physicians was mentioned in JAMA , decried in Science . They were more explicit. Science was more forthcoming and did not mince words at all. They mention repression, active antisemitism, and the weaponization of education. That was probably what alarmed Science most.

JAMA was more interested in the persecution of Jewish physicians, especially the restriction of their practice, of their education, and the consequences of laws that were persecutory in nature. And this was two years earlier than the publication of the Davis and Krueger paper.

Your critique of the Davis paper was that it focused on economic issues and read as if nothing outrageous was happening outside of the economic sphere?

Brandt: The Davis piece is remarkable for its opacity, its ability to focus on a reform and not have any context around it. Davis' response to one critic of the article makes that clear. He said, "Of course I'm concerned about what's going on with Jews in Germany. But we were writing about a social reform, a health reform."

The kind of denial that it takes to dissociate the social and political context from what you're centering your attention on is why we use the term "compartmentalization." These are the psychological and institutional structures that permitted racism to persist.

Joelle and I explored the fact that Davis had done much for the poor. He was trying to expand insurance coverage in the U.S., so in this instance, this narrowness was really shocking, especially given the fact that his ancestors were Jewish.

Was there a change among the editors after the war when coverage changed?

Abi-Rached: The evidence was so obvious that the doctors were part and parcel of the genocidal nature of that regime that a journal like NEJM could not remain silent. It's an important moment in the history of medical practice and medical research that had a profound effect on how experiments were conducted later on, in the second half of the 20th century.

A paradigm shift happened: You could not be silent and blind and not engage with what was happening especially because it concerned medical practitioners. It also laid bare how the Hippocratic Oath was insufficient to protect patients or anyone else. There was a clash between the very paternalistic nature of the Hippocratic Oath and how institutions, even regimes, can politicize that oath to their own advantage and how medical doctors are enmeshed in that institutional framework, whether they serve the state or an insurance scheme.

NEJM could not remain silent, and it is only in the 1960s onwards that you come across editorials, perspectives on the ethics of medical experimentation, and so on.

Are there lessons for today here?

Abi-Rached: An important conclusion is that silence is not neutral. It says as much as it hides. Reading the past also tells us something about our contemporary moment, our failings, including our moral failings.

Another point is that medicine cannot be dissociated from social and political issues. They are intertwined. Medicine is the product of societal beliefs, norms, and prejudices. NEJM is a reflection of wider social, political, and moral biases. It's a reflection of a wider society.

Journal information: New England Journal of Medicine

Provided by Harvard Gazette

This story is published courtesy of the Harvard Gazette , Harvard University's official newspaper. For additional university news, visit Harvard.edu .

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• Open access
• Published: 20 May 2024

Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats

• Jakob Steinfeldt   ORCID: orcid.org/0000-0003-1387-2054 1 , 2 , 3 , 4 , 5   na1 ,
• Benjamin Wild   ORCID: orcid.org/0000-0002-7492-8448 6   na1 ,
• Thore Buergel 5 , 6   na1 ,
• Maik Pietzner   ORCID: orcid.org/0000-0003-3437-9963 3 , 7 , 8 ,
• Julius Upmeier zu Belzen   ORCID: orcid.org/0000-0002-0966-4458 6 ,
• Andre Vauvelle 9 ,
• Stefan Hegselmann   ORCID: orcid.org/0000-0002-2145-3258 10 , 11 ,
• Spiros Denaxas 9 , 12 , 13 , 14 ,
• Harry Hemingway   ORCID: orcid.org/0000-0003-2279-0624 9 , 13 , 14 ,
• Claudia Langenberg   ORCID: orcid.org/0000-0002-5017-7344 3 , 7 , 8 ,
• Ulf Landmesser   ORCID: orcid.org/0000-0002-0214-3203 1 , 2 , 4 , 15 , 16   na2 ,
• John Deanfield 5   na2 &
• Roland Eils   ORCID: orcid.org/0000-0002-0034-4036 6 , 17   na2  

Nature Communications volume  15 , Article number:  4257 ( 2024 ) Cite this article

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• Disease prevention
• Epidemiology

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1883 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,460 UK Biobank. Importantly, we observed discriminative improvements over basic demographic predictors for 1774 (94.3%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1347 (89.8%) of 1500 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

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Introduction.

The early phase of the COVID-19 pandemic exposed a global deficiency in delivering systematic, data-driven guidance for individual patients and healthcare providers with critical implications for pandemic preparedness. The assessment of an individual’s risk for future disease is central to guiding preventive interventions, early detection of disease, and the initiation of treatments. However, bespoke risk scores are only available for a subset of common diseases 1 , 2 , 3 , 4 , leaving healthcare providers and individuals with little to no guidance on most relevant diseases. Even for diseases with established risk scores, little consensus exists on which score to use and associated physical or laboratory measurements to obtain, leading to highly fragmented practice in routine care 5 . Importantly, in the early phases of emerging pandemics such as COVID-19, it is necessary to allocate sparse resources, but risk scores to identify vulnerable subpopulations are not available due to the lack of available data.

At the same time, most medical decisions on diagnosis, treatment, and prevention of diseases are fundamentally based on an individual’s medical history 6 . With the widespread digitalization, this information is routinely collected by healthcare providers, insurance, and governmental organizations at a population scale in the form of electronic health records 7 , 8 , 9 , 10 , 11 , 12 . These readily accessible records, which include diseases, medications, and procedures, are potentially informative about future risk trajectories, but their potential to improve medical decision-making is limited by the human ability to process and understand vast amounts of data 13 .

To date, routine health records have been used to guide clinical decision-making with etiological 14 , 15 , 16 , 17 , diagnostic 18 , 19 , and prognostic research 15 , 16 , 20 , 21 , 22 . Existing efforts often extract and leverage known clinical predictors with new methodologies 19 , augment them with additionally extracted data modalities such as clinical notes 23 , or aim to identify novel predictors among the recorded concepts 14 , 15 , 16 , 17 . Prior work on the prediction of disease onset has mainly focused on single diseases, including dementia 15 , 24 , cardiovascular conditions 23 , 25 such as heart failure 26 and atrial fibrillation 27 , 28 . In contrast, phenome-wide association studies (PheWAS) quantifying the associations of genetic variants with comprehensive phenotypic traits are emerging in genetic epidemiology 29 , 30 . While approaches have been developed for high-throughput phenotyping 31 , 32 and to extract information from longitudinal health records 33 , 34 , no studies have investigated the predictive potential and potential utility over the entire human phenome. Consequently, the predictive information in routinely collected health records and its potential to systematically guide medical decision-making is largely unexplored.

Here, we examined the predictive potential of an individual’s entire medical history and propose a systematic approach for phenome-wide risk stratification. We developed, trained, and validated a neural network in the UK Biobank cohort 35 to estimate disease risk from routinely collected health records. Unlike alternative methods, such as linear models or survival trees, which require separate models for each disease, our approach employs a multi-layer perceptron that predicts multiple endpoints concurrently, resulting in a significantly simplified model architecture. These endpoints include preventable diseases (e.g., coronary heart disease), diseases that are not currently preventable, but the early diagnosis has been shown to substantially slow down the progression and development of complications (e.g., heart failure), and outcomes, which are currently neither entirely preventable nor treatable (e.g., death). They also include both diseases with risk prediction models recommended in guidelines and used in practice (e.g., cardiovascular diseases or breast cancer) as well as diseases without current risk prediction models (e.g., psoriasis and rheumatoid arthritis).

We evaluated our approach by integrating the endpoint-specific risk states estimated by the neural network in Cox Proportional Hazard models 36 , investigating the phenome-wide predictive potential over basic demographic predictors, selected comorbidities, and established modifiable risk factors, and illustrating how phenome-wide risk stratification could benefit individuals by providing risk estimates, facilitating early disease diagnosis, and guiding preventive interventions. Furthermore, by externally validating in the All Of Us cohort 37 , we show that our models can generalize across healthcare systems and populations, including communities historically underrepresented in biomedical research.

Finally, we assessed the potential of our approach to aid risk stratification for the primary prevention of cardiovascular disease and to respond to emerging health threats at the example of COVID-19. We then show that the risk states of pneumonia, sepsis & all-cause death can be used to calculate a combined severity risk score using primary and secondary care records available before the global spread of the COVID-19 pandemic. Our results demonstrate the currently unused potential of routine health records to guide medical practice by providing comprehensive phenome-wide risk estimates.

Characteristics of the study population and integration of routine health records

This study is based on the UK Biobank cohort 35 , 38 , a longitudinal population cohort of 502,460 relatively healthy individuals of primarily British descent, with a median age of 58 (IQR 50, 63) years, 54.4% biological females, 11% current smokers, and a median BMI of 26.7 (IQR 24.1, 29.9) at recruitment (Table  1 for detailed information). Individuals recruited between 2006 and 2010 were followed for a median of 12.6 years, resulting in ~6.2 M overall person-years on 1883 phenome-wide endpoints 39 with ≥ 100 incident events (>0.02% of individuals having the event in the observation time). We externally validated our findings in individuals from the All of Us cohort, a longitudinal cohort of 229,830 individuals with linked health records recruited from all over the United States. Individuals in the All of Us cohort are of diverse descent, with 46% of reportedly non-white ethnicity and 78% of groups historically underrepresented in biomedical research 37 , 40 , and have a median age of 54 (IQR 38, 65) years with 61.1% biological females (see Table  1 for detailed information). Individuals were recruited from 2019 on and followed for a median of 3.5 years, resulting in ~787,300 person-years on 1568 endpoints.

Central to this study is the prior medical history, defined as the entirety of routine health records before recruitment. Before further analysis, we mapped all health records to the OMOP vocabulary. While most records originate from primary care and, to a lesser extent, secondary care (Suppl. Figure  1a ), the predominant record domains are drugs and observations, followed by conditions, procedures, and devices (Supplementary Fig.  1b ). Interestingly, while rare medical concepts (with a record in <1% of individuals in the study population) are not commonly included in prediction models 21 , they are often associated with high incident event rates (exemplified by the mortality rate in Supplementary Fig.  1c ) compared to common concepts (a record present in >= 1% of the study population). For example, the concept code for “portal hypertension” (OMOP 34742003) is only recorded in 0.04% (203) of individuals at recruitment, but 48.7% (99 individuals) will die over the course of the observation period. Importantly, there are many distinct rare concepts, and thus 91.7% of individuals have at least one rare record before recruitment, compared with 92.5% for common records. In addition, 60.7% of individuals have ≥ 10 rare records compared with 78.4% for common records, and individuals have only slightly fewer rare than common records (Supplementary Fig.  1d ).

After excluding very rare concepts (<0.01%, less than 50 individuals with the record in this study), we integrated the remaining 15,595 unique concepts (Supplementary Data  2 ) with a multi-task multi-layer perceptron (with 88.4 M parameters) to predict the phenome-wide onset of 1883 endpoints (Supplementary Data  1 ) simultaneously (Fig.  1a ). For comparison, we also include additional comparisons with a linear baseline (with 29.4 M parameters, Supplementary Fig.  2 ), demonstrating superior performance at a minimal increase of complexity.

a The medical history captures encounters with primary and secondary care, including diagnoses, medications, and procedures (ideally) from birth. Here we train a multi-layer perceptron on data before recruitment to predict phenome-wide incident disease onset for 1883 endpoints. b Location and size of the 22 assessment centers of the UK Biobank cohort across England, Wales, and Scotland. c To learn risk states from individual medical histories, the UK Biobank population was partitioned by their respective assessment center at recruitment. d For each of the 22 partitions, the Risk Model was trained to predict phenome-wide incident disease onset for 1883 endpoints. Subsequently, for each endpoint, Cox proportional hazard (CPH) models were developed on the risk states in combination with sets of commonly available predictors to model disease risk. Predictions of the CPH model on the test set were aggregated for downstream analysis. e External validation in the All of US cohort. After mapping to the OMOP vocabulary, we transferred the trained risk model to the All of US cohort and calculated the risk state for all endpoints. To validate these risk states, we compared the unchanged CPH models developed in the UK Biobank with refitted CPH models for age and sex. Source data are provided. The Icons are made by Freepik from www.flaticon.com .

To ensure that our findings are generalizable and transferable, we spatially validate our models in 22 recruitment centers (Fig.  1b ) across England, Wales, and Scotland. We developed 22 models, each trained on individuals from 21 recruitment centers at recruitment, randomly split into training and validation sets (Fig.  1c ). We subsequently tested the models on individuals from the additional recruitment center unseen for model development for internal spatial validation. After checkpoint selection on the validation data sets and obtaining the selected models’ final predictions on the individual test sets, the test set predictions were aggregated for downstream analysis (Fig.  1d ). Subsequently, disease-specific exclusions of prior events and sex-specificity were respected in all downstream analyses. After development, the models were externally validated in the All of Us cohort 37 .

Routine health records stratify phenome-wide disease onset

Central to the utility of any predictor is its potential to stratify risk. The better the stratification of low and high-risk individuals, the more effective targeted interventions and disease diagnoses are.

To investigate whether health records can be used to identify high-risk individuals, we assessed the relationship between the risk states estimated by the neural network for each endpoint and the risk of future disease (Fig.  2 ). For illustration, we first aggregated the incident events over the percentiles of the risk states for each endpoint and subsequently calculated ratios between the top and bottom 10% of risk states over the entire phenome (Fig.  2a ). We found that fewer than 10% of the individuals had an incident hypertension diagnosis in the observation window if they were estimated to be in the bottom risk percentile of the medical history, compared to more than 60% if they were estimated to be in the top risk percentile. Subsequently, the incident event ratio between the top and bottom deciles was ~5.23. Importantly, we found differences in the event rates, reflecting a stratification of high and low-risk individuals for almost all endpoints covering a broad range of disease categories and etiologies: For 1341 of 1883 endpoints (71.2%), we observed >10-times as many events for individuals in the top 10% of the predicted risk states compared to the bottom 10%. For instance, these endpoints included rheumatoid arthritis (Ratio ~11.3), ischemic heart disease (Ratio ~23.5), or chronic obstructive pulmonary disease (Ratio ~65.4). For 230 (12.2%) of the 1883 conditions, including abdominal aortic aneurysm (Ratio ~163.4), more than 100 times the number of individuals in the top 10% of predicted risk states had incident events compared to the bottom 10%. For 542 (28.8%) endpoints, the separation between high and low-risk individuals was smaller (Ratio <10), which included hypertension (Ratio ~5.2) and anemia (Ratio ~6.7), often diagnosed earlier in life or precursors for future comorbidities. Notably, the ratios were >1 for all but one of the 1883 investigated endpoints, even though all models were developed in spatially segregated assessment centers. To illustrate how high-risk individuals differ from the moderate cases, we also provide additional ratios comparing the top 10% to individuals in the median 20% of the population. The complete list of all endpoints and corresponding statistics can be found in Supplementary Data  4 .

a Ratio of incident events in the Top 10% compared with the Bottom 10% of the estimated risk states. Event rates in the Top 10% are higher than in the Bottom 10% for all but one of the 1883 investigated endpoints. Red dots indicate 24 selected endpoints detailed in Fig. 2b. To illustrate, 1198 (2.39%) individuals in the top risk decile for cardiac arrest experienced an event compared with only 30 (0.06%) in the bottom decile, with a risk ratio of 39.93. b Incident event rates for each medical history risk percentile (if medical history was available) for a selection of 24 endpoints. c Cumulative event rates with 95% confidence intervals for the Top 1%, median, and Bottom 1% of risk percentiles in b ) over 15ys. Statistical measures were derived from 502.460 individuals. Individuals with prevalent diseases were excluded from the endpoints-specific analysis. Source data are provided.

In addition to the phenome-wide analysis of 1883 endpoints, we also provide detailed associations between the risk percentiles and incident event ratios (Fig.  2b ), as well as cumulative event rates for up to 15 years (Fig.  2c ) of follow-up for the top, median, and bottom percentiles for a subset of 24 selected endpoints. This set was selected to comprise actionable endpoints and common diseases with significant societal burdens, specific cardiovascular conditions with pharmacological and surgical interventions, as well as endpoints without established tools to stratify risk to date. To exemplify the potential of our approach, among individuals in the top risk decile for heart failure, 8018 (16.06%) experienced an event, in contrast to 178 (0.35%) individuals in the bottom decile, resulting in a risk ratio of 46.35 (Fig.  2a, b , Supplementary Data  4 ). Consequently, those at high risk of heart failure could be prioritized for echocardiographic screening and, if necessary, prescribed effective guideline-directed medical therapy. Similarly, individuals with a high risk of developing COPD—where the top 10% face over 65 times the risk compared to the bottom 10%—may be considered for spirometry, an approach already established in the CAPTURE trial 41 . If confirmed, they could benefit from interventions such as long-acting bronchodilators. As a third example, a high-risk estimate for less common diseases, such as multiple sclerosis (risk ratio ~8.3), could further support referring individuals to a specialist and potentially shorten the often extensive patient journey before a final diagnosis is reached.

In summary, the disease-specific states stratify the risk of onset for all 1883 investigated endpoints across clinical specialties. This indicates that routine health records provide a large and widely unused potential for the systematic risk estimation of disease onset in the general population.

Discriminative performance indicates potential utility

While routine health records can stratify incident event rates, this does not prove utility. To test whether the risk state derived from the routine health records could provide utility and information beyond ubiquitously available predictors, we investigated the predictive information over age and biological sex, selected comorbidities from the Charlson Comorbidity Index 42 , and established modifiable risk factors from the AHA ASCVD pooled cohort equation 3 . We modeled the risk of disease onset using Cox Proportional-Hazards (CPH) models for all 1883 endpoints, which allowed us to estimate adjusted hazard ratios (denoted as HR in Supplementary Data  6 ) and 10-year discriminative improvements (indicated as Delta C-index in Fig.  3a ).

a Differences in discriminatory performance quantified by the C-Index between CPH models trained on Age+Sex and Age+Sex+MedicalHistory for all 1883 endpoints. We found significant improvements over the baseline model (Age+Sex, age, and biological sex only) for 1774 (94.2%) of the 1883 investigated endpoints. Red dots indicate selected endpoints in Fig. 3b. b Absolute discriminatory performance in terms of C-Index comparing the baseline (Age+Sex, black point) with the added routine health records risk state (Age+Sex+RiskState, red point) for a selection of 24 endpoints. c The direct C-index differences for the same models. Dots indicate medians and whiskers extend to the Bonferroni-corrected 95% confidence interval for a distribution bootstrapped over 100 iterations. d Example of individual predicted phenome-wide risk profile. Predisposition (10-year risk estimated by Age+Sex+RiskState compared to risk estimated by Age+Sex alone) is displayed in the inner circle, and absolute 10-year risk estimated by Age+Sex+RiskState can be found in the outer circle. Labels indicate endpoints with a high individual predisposition (>2 times higher than the Age+Sex-based reference estimate) and absolute 10-year risk > 10%. e Top 5 highest attributed records for selected endpoints. Statistical measures were derived from 502.460 individuals. Source data are provided.

We found significant improvements over the baseline model (age and biological sex only) for 1774 (94.2%) of the 1883 investigated endpoints (Fig.  3 , Supplementary Data  5 ). For many of these endpoints, the discriminative improvements were considerable (Delta C-Index Q25%: 0.094, Q50: 0.116, Q75: 0.141). We found significant improvements for 23 of the highlighted subset of 24 endpoints (indicated in Fig.  2a ), with the largest increases for the prediction of back pain (Delta C-Index: +0.238 (CI 0.236, 0.241)), suicide attempts (Delta C-Index: +0.224 (CI 0.213, 0.235)), psoriasis (Delta C-Index: +0.171 (CI 0.161, 0.178)), all-cause mortality (Delta C-Index: +0.171 (0.169, 0.174)) and chronic obstructive pulmonary disease (Delta C-Index: +0.156 (0.151, 0.159)). In contrast, we did not find significant improvements in the prediction of 86 (4.6%) of the 1883 endpoints, including, e.g., Parkinson’s disease (Delta C-Index: −0.006 (CI −0.013, 0)) or even deteriorations in the prediction of 23 (1.2%) of the endpoints, including neoplasm like cervical cancer (Delta C-Index: −0.025 (−0.059, −0.004)) and gastrointestinal diseases as chronic hepatitis (Delta C-Index: −0.032 (−0.064, −0.007)).

We also present a comparison between our approach and the Charlson Comorbidity Index’s 42 predictive performance, both of which can be automated. Additionally, we compare our method to the well-established ASCVD predictors, which are widely accessible but require an additional blood draw. Notably, incorporating the comorbidities from the Charlson Comorbidity Index enhances the discriminative capacity beyond age and sex; however, adding medical history proves to be significantly more effective in improving performance (Supplementary Fig.  3 , Supplementary Data  5 ). Likewise, while supplementing ASCVD predictors to age and sex augments the performance for most endpoints, it remains inferior to the combination of age, sex, and medical history alone. Incorporating the medical history alongside the comorbidities or ASCVD predictors further improves the predictive performance for the vast majority of endpoints (AgeSex+Comorbidities augmented by the MedicalHistory: +1726/1883 (91.7%), ASCVD+MedicalHistory: +1727/1883 (91.7%), demonstrating complementary nature of these information sources.

For illustration, we also present individual phenome-wide risk profiles (Fig.  3c , Supplementary Fig.  4a +b and 5a+b). The risk profiles varied substantially in the predispositions relative to the age and sex reference (the inner circle, see methods for details) and the absolute 10-year risk estimates (the outer circle). The first individual (Fig.  3c ), a 60-year-old man, is predicted to be at a particularly high 10-year risk of metabolic, cardiovascular, respiratory, and genitourinary conditions, including diabetes mellitus (19.4%), heart failure (22%), COPD (14.9%), and chronic kidney disease (16.8%). Increased risk of neoplastic, dermatological, and musculoskeletal conditions was not predicted by the prior health records of this individual. In contrast, another individual, a 48-year-old woman (Supplementary Fig.  5b ), is not estimated at increased cardiovascular risk but conversely to have almost 10x the risk for suicide ideation and attempt or self-harm compared to the reference group.

Importantly, the model performance is robust to the removal of recent information, indicating that the model effectively incorporates both the individuals’ long-term medical history and recent interactions with the healthcare system in order to predict future disease onset (Supplementary Fig.  6 ). We provide Shapley attributions 43 for the most important records (Fig.  3d , Suppl. Figure  4c , Suppl. Figure  5c ) and all records for the 24 highlighted endpoints (Supplementary Data  9 ) in the study population, enhancing the interpretability of our findings.

These findings indicate that health records contain substantial predictive information over established predictors for the majority of disease endpoints from across clinical specialties.

Predictive models can generalize across healthcare systems and populations

While our findings indicate potential utility in the UK Biobank, health records vary substantially across healthcare systems and over time due to differences in medical and coding practices (“distribution shift”) and underlying differences in the populations. Thus, predictive models can fail to learn robust and generalizable information 44 , 45 , 46 .

To better understand the generalisability across different healthcare systems, we predicted risk states and absolute risk estimates for all individuals in the All of Us cohort with linked medical records ( N  = 229,830; see Table  1 ). Importantly, we found significant improvements over the baseline model (age and biological sex only) for 1347 (85.9%) of the 1568 investigated endpoints with at least 100 incident events (Fig.  4a , Supplementary Data  8 ), replicating 1347/1500 (89.8%) of all significant improvements in the UK Biobank (Fig.  4b , Supplementary Data  8 ). Generally, larger improvements in the UK Biobank were replicated in the All of Us cohort. It’s noteworthy that smaller improvements in the UK Biobank often corresponded to proportionately larger improvements in All of Us, while larger improvements in the UK Biobank were attenuated in All of Us (Fig.  4c ).

a External validation of the differences in discriminatory performance quantified by the C-Index between CPH models trained on age and biological sex and age, biological sex, and the risk state for 1.568 endpoints in the All of Us cohort. We find significant improvements over the baseline model (age and biological sex only) for 1.347 (85.9%) of the 1.568 investigated endpoints. b Direct comparison of the absolute C-Index in the UK Biobank (x-axis) and the All Of Us cohort (y-axis). Significant improvements can be replicated for 1347 (89.8%, green points) of 1500 endpoints in the All Of Us cohort. c Comparison of mean delta C-Index per delta percentile (derived from the UK Biobank from the 1.568 endpoints available in All Of Us). Improvements in the All Of Us cohort are consistent with the UK Biobank cohort: Small improvements in the UK Biobank tend to be larger in All Of Us, while large improvements in the UK Biobank tend to be attenuated in All Of Us. d Distribution of C-Indices for the 1.568 investigated endpoints stratified by communities historically underrepresented in biomedical research (UPD) 73 . Dots indicate medians and whiskers extend to the Bonferroni-corrected 95% confidence interval for a distribution bootstrapped over 100 iterations. e For the same groups, confidence intervals for the additive performance as measured by the C-Index compared to the baseline model. Dots indicate medians and whiskers extend to the Bonferroni-corrected 95% confidence interval for a distribution bootstrapped over 100 iterations. f Absolute discriminatory performance in terms of C-Index comparing the baseline (age and biological sex, black point) with the added routine health records risk state (red points) for a selection of 24 endpoints. g The differences in C-index for the same models. Statistical measures for UKB (in b and c ))were derived from 502.460 individuals and for AoU (in a – g ) were derived from 229.830 individuals. Dots indicate medians and whiskers extend to the Bonferroni-corrected 95% confidence interval for a distribution bootstrapped over 100 iterations. Source data are provided.

As the risk states were largely derived from white, middle-aged, and generally affluent and healthy individuals from the UK, it was critical to validate the discriminative performance in diverse and historically underserved and underrepresented groups and ethnicities. Generally, we found comparable discriminative performances (Fig.  4d ) and substantial benefits over basic demographic predictors (example of cardiac arrest in Fig.  4e ) across all investigated groups.

To illustrate these improvements further, we replicated significant improvements for all of the 24 a priori selected endpoints, with improvements ranging from modest for hypertension (Delta C-Index: +0.021 (0.016, 0.024)) and Parkinson’s disease (Delta C-Index: +0.035 (0.021, 0.05)) to substantial for, e.g., All-Cause Death (Delta C-Index: +0.116 (0.104, 0.127), Pulmonary embolism (Delta C-Index: +0.125 (0.112, 0.137)), and Cardiac arrest (Delta C-Index: +0.176 (0.146, 0.206)) (Fig.  4f, g and Supplementary Data  8 ). Only for a subset of 54 (3.44%) significantly improved endpoints in the UK Biobank, the discriminative performance in All Of Us deteriorated significantly upon transferring the pre-trained medical history risk model and integrating the information beyond age and biological sex alone, including hepatitis (Delta C-Index: −0.226 (−0.251, −0.2)), substance abuse (Delta C-Index: −0.037 (−0.05, −0.026)) and osteoporosis (Delta C-Index: −0.015 (−0.021, −0.008)).

Taken together, our findings suggest that predictive models based on medical history can generalize across health systems and are robust to diverse populations.

Predictions can support cardiovascular disease prevention and the response to emerging health threats

While comprehensive phenome-wide risk profiles provide opportunities to guide medical decision-making, not all of the predictions are actionable. To illustrate the potential clinical utility, we focused on the primary prevention of cardiovascular disease and the response to newly emerging health threats at the example of COVID-19.

Risk scores are well established in the primary prevention of cardiovascular events and have been recommended to guide preventive lipid-lowering interventions 47 . While cardiovascular predictors are accessible at a low cost, dedicated visits and resources from healthcare providers for physical and laboratory measurements are required. Therefore, we compared our phenome-wide risk score, based only on age, sex, and routine health records, to models based on established cardiovascular risk scores, the SCORE2 48 , the ASCVD 3 , and the British QRISK3 4 score. Interestingly, the discriminative performance of our phenome-wide model is competitive with the established cardiovascular risk scores for all investigated cardiovascular endpoints (Fig.  5a , Supplementary Data  7 ): we found comparable C-Indices with differences +0.001 (CI −0.002, 0.005) for ischemic stroke, +0.002 (CI 0.002, 0.005) for ischemic heart disease and +0.006 (CI 0.003, 0.009) for myocardial infarction compared with the comprehensive QRISK3 score. It is noteworthy that these discriminative improvements are substantially better for later-stage diseases, including heart failure (+0.018 (CI 0.015, 0.021)), cardiac arrest (+0.05 (CI 0.042, 0.059)), and all-cause mortality (+0.13 (CI 0.128, 0.132)) when prior health records are considered.

a Discriminatory performances in terms of absolute C-Indices comparing risk scores (Age+Sex, SCORE2, ASCVD, and QRISK as indicated, black point) with the risk model based on Age+Sex+RiskState (red segment). b Direct differences between risk scores (Age+Sex, SCORE2, ASCVD, and QRISK as indicated) and the risk model based on Age+Sex+RiskState in terms of C-index. Dots indicate medians and whiskers extend to the Bonferroni-corrected 95% confidence interval for a distribution bootstrapped over 100 iterations. c Estimated cumulative event trajectories, including 95% confidence intervals of severe (with hospitalization) and fatal (death registry) COVID-19 outcomes stratified by the Top, Median, and Bottom 5% based on age (left) or risk states of pneumonia, sepsis, and all-cause mortality as estimated by Kaplan-Meier analysis. Statistical measures were derived from 502.460 individuals. Source data are provided.

To further illustrate potential utility, we look at newly emerging pathogenic health threats, where rapid and reliable risk stratification is required to protect high-risk groups and prioritize preventive interventions. We investigated how our phenome-wide risk states could have been used in the context of COVID-19, a respiratory infection with pneumonia and sepsis as common, life-threatening complications of severe cases. We repurposed the risk states for pneumonia, sepsis, and all-cause mortality to calculate a combined COVID-19 severity risk score using information available at the end of 2019 before the global spread of the COVID-19 pandemic (see Methods for details). The COVID-19 severity risk score resembles the risk for developing severe or fatal COVID-19 and illustrates how health records could have helped to identify individuals at high risk and to prioritize individuals in initial vaccination campaigns better. Augmenting age with the COVID-19 severity risk score, we found substantially improved discriminative performance for both severe and fatal COVID-19 outcomes (Severe: C-Index (age) 0.597 (CI 0.591, 0.604) → C-Index (age + COVID-19 severity risk score) 0.647 (CI 0.641, 0.654); Fatal: C-Index (age) 0.720 (CI 0.710, 0.731) → C-Index (age + COVID-19 severity risk score) 0.780 (CI 0.772, 0.789). These discriminative improvements translate into higher cumulative incidence in the Top 5% population compared to age alone (Suppl. Figure  6C , age (left), COVID-19 severity score (right), severe COVID-19 (top), fatal COVID-19 (bottom)): In the top 5% of the age-based risk group (~79 (IQR 77, 81) years old), 0.42% (CI 0.34%, 0.5%, n  = 105) have been hospitalized, and 0.26% (CI 0.2%, 0.33%, n  = 66) had died by the end of the first wave. By the end of the second wave, around 0.96% (CI 0.83%, 1.08%, n = 240) had been hospitalized, and 0.44% (0.36%, 0.52%, n  = 111) had died. In contrast, for individuals in the top 5% of the COVID-19 severity risk score, by the end of the first wave, around 0.64% (CI 0.54%, 0.74%, n  = 160) had been hospitalized, and 0.32% (0.25%, 0.39%, n  = 80) had died, while by the end of the second wave, 1.74% (CI 1.57%, 1.9%, n  = 436) had been hospitalized and 0.68% (0.58%, 0.79%, n  = 172) had died.

In summary, our findings illustrate the clinical utility of medical history for primary prevention of cardiovascular diseases and the rapid response to emerging health threats.

Current clinical practice lacks systematic, data-driven guidance for individuals and care providers. Our study demonstrated that medical history can systematically inform on phenome-wide risk across clinical specialties, as shown in the British UK Biobank cohort. Subsequently, we show that these risk states can be repurposed to identify individuals vulnerable to severe COVID-19 and mortality. Importantly, we found significant improvements in the discriminated performance for the vast majority of disease endpoints, of which almost 90% could be replicated in the US All of US cohort. Our results indicated utility beyond age, sex, selected comorbidities, and established cardiovascular risk factors commonly considered in clinical practice for preventable diseases, treatable diseases, and diseases without existing risk stratification tools. We anticipate that our approach has the potential to facilitate population health at scale.

Designed for outpatient settings and focused on patients without acute complaints, our approach identifies incident disease onset from early (e.g., hypertension) and later (e.g., bypass surgery) health system contacts. We identified three primary scenarios of potential utility: Firstly, medical history can be exploited in diseases that are preventable with effective interventions, such as the prescription of lipid-lowering medication for primary prevention of coronary heart disease 47 . Lowering LDL cholesterol in 10,000 individuals at increased risk by 2 mmol/L with atorvastatin 40 mg daily (~2€ per month) for 5 years is estimated to prevent 500 vascular events, reducing the individual relative risk by more than a third 49 , 50 . Secondly, in conditions that are not preventable anymore individuals can benefit from early detection and treatment, like in type 2 diabetes or systolic heart failure. In individuals with heart failure with reduced ejection fraction, a comprehensive treatment regime (including ARNI, beta-blockers, MRA, and SGLT2 inhibitors) compared to a conventional regime (ACEi or ARB and beta blockers) reduced the hospital admissions for heart failure by more than two thirds, all-cause mortality by almost half  51 . For a 55-year-old male, this translated into an estimated 8.3 additional years free from cardiovascular death or readmission for heart failure. Lastly, in cases where outcomes are neither preventable nor treatable, estimates of prospective individual risk may be of high importance for personal decisions or the planning of advanced care, e.g., a high short-term mortality could identify patients needing to transition from curative to palliative strategies for optimal care 52 , 53 . Multiple studies have shown that palliative care services can improve patients’ symptoms and life quality and may even increase survival 54 . Overall, our approach could facilitate the identification of high-risk populations for specific screening programs, potentially improving the value of national health programs.

Importantly, our approach, based on routine health records, shows large discriminative improvements for the majority of diseases compared with conventionally tested biomarkers 55 , 56 , 57 and can generalize across diverse health systems, populations, and ethnicities. However, we also see that including the medical history over age and sex deteriorated the performance for a subset of 1.2% (UK Biobank) and 4.9% (All Of Us cohort), respectively. Three central challenges remain: First, health records, being products of interactions with the medical system, are subject to biological, procedural, and socio-economic biases 58 , as well as being dependent on the evolving nature of medical knowledge and policies. Furthermore, certain measurements and laboratory values are often inaccessible at the point of care, and harmonization in and across health systems presents a significant barrier to implementation 59 . Integrating these measures into the model holds considerable promise to improve the predictive performance further. While our approach is based on the standardized OMOP vocabulary, implementation requires a robust harmonization infrastructure, and data drift might necessitate model updates. Second, research cohorts often comprise healthier individuals with lower disease prevalence than the general population 60 , potentially leading to underestimating absolute risks. While discriminative improvements provide evidence of the potential clinical utility, they are insufficient to prove it, as it is highly context-dependent on the population, the disease, and the interventions available. This is particularly relevant for very rare diseases, where screening the general population poses the risk of false positive findings. Future randomized implementation studies must investigate how this discriminatory information can translate into improved clinical outcomes in the respective target populations. The third challenge concerns ensuring the interpretability of our approach on such complex data. Our approach provided unique insights into how the model used patients’ medical history to make risk predictions. The Shapley value attributions highlighted features the model found most informative for inference on both individual and population levels. These attributions are reflective of the model’s decision-making process, and while they aligned with our clinical understanding, they should not replace clinician judgment or other forms of evidence. As we refine and deploy this approach, we must remain vigilant in evaluating its performance and understanding the interpretational limitations. Interestingly, the attributions also expose the challenges of implementing predictive models across primary care and clinical specialties. For example, statins and chest pain are among the most highly attributed records for a high future likelihood of developing heart disease, indicating that in some cases, prior healthcare providers have already considered or even acted upon a high suspected risk of the disease, without entering the actual diagnosis into the records. Consequently, employing the model for such patients, when low-density lipoprotein (LDL) cholesterol levels are already managed, may not lead to further preventive actions if the patient’s care aligns with established standards. Importantly, we find that such cases do not drive the model’s predictive performance by assessing the robustness of the model performance to the removal of recent information (Supplementary Fig.  6 ). Ultimately, if routine health records are to be used for risk prediction, robust governance rules to protect individuals, such as opt-out and usage reports, need to be implemented. With many national initiatives emerging to curate routine health records for millions of individuals in the general population, future studies will allow us to better understand how to overcome these challenges.

Our study presents a systematic approach to simultaneous risk stratification for thousands of diseases across clinical specialties based on readily available medical history. These risk states can then be used to rapidly respond to emerging health threats such as COVID-19. Our findings demonstrate the potential to link clinical practice with already collected data to inform and guide preventive interventions, early diagnosis, and treatment of disease.

Data source and definitions of predictors and endpoints

To derive risk states, we analyzed data from the UK Biobank cohort. Participants were enrolled from 2006 to 2010 in 22 recruitment centers across England, Scotland, and Wales; the follow-up is ongoing, and records until the 24th of September 2021 are included in this analysis. The UK Biobank cohort comprises 273.353 women and 229.107 men aged between 37-73 years at the time of their assessment visit. Participants are linked to routinely collected records from primary care (GP), hospital records (HES, PEDW, and SMR), and death registries (ONS), providing longitudinal information on diagnosis, procedures, and prescriptions for the entire cohort from Scotland, Wales, and England. Routine health records were mapped to the OMOP CDM and represented as a 71.036-dimensional binary vector, indicating whether a concept has been recorded at least once in an individual prior to recruitment. A subset of 15.595 unique concepts, all found in at least 50 individuals, was chosen for model development. Endpoints were defined as the set of PheCodes X 39 , 61 , and after the exclusion of very rare endpoints (recorded in <100 individuals), 1883 PheCodes X endpoints were included in the development of the models. Due to the adult population, congenital, developmental, and neonatal endpoints were excluded. For each endpoint, subsequently, time-to-event outcomes were extracted, defined by the first occurrence after recruitment in primary care, hospital, or death records. Detailed information on the predictors and endpoints is provided in Supplementary Data  1 - 2 .

While all individuals in the UK Biobank were used to integrate the routine health records, develop the model, and estimate phenome-wide log partial hazards, individuals were excluded from endpoint-specific downstream analysis if they were already diagnosed with a disease (defined by a prior record of the respective endpoint) or are generally not eligible for the specific endpoint (females were excluded from the risk estimation for prostate cancer).

To externally validate our risk states, we investigate individuals from the All of Us cohort 37 , containing information on 229,830 individuals of diverse descent and from minorities historically underrepresented in biomedical research 40 . Because we only use the All of Us cohort for validation, we evaluate the predictive performance for the subset of 1568 endpoints with at least 100 incident events in the All of Us cohort.

The study adhered to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement for reporting 62 . The completed checklist can be found in the Supplementary Information.

Extraction and preparation of the routine health records

To extract the routine health records of each individual, we first aggregated the linked primary care, hospital records, and mortality records and mapped the aggregated records to the OMOP CDM (mostly SNOMED and RxNorm). Specifically, we used mapping tables provided by the UK Biobank, the OHDSI community, and SNOMED International to map concepts from the provider and country-specific non-standard vocabularies to OMOP standard vocabularies.

We restricted the analysis to the domains “Observation”, “Condition”, “Procedure”, “Drug” and “Device”. To reduce the complexity, we did not include any laboratory measures. The PheCode X endpoints 39 , 61 were derived from either mapping directly from ICD-10 (hospital and death records) or mapping from SNOMED to ICD-10 (using the official mapping table) and subsequently to Phecodes X.

To ensure the accuracy and integrity of our data, we implemented multiple validation steps. After each stage in the extraction and mapping process, we conducted plausibility and sanity checks on the distribution of the mapped records, along with spot checks of individual records. This approach was critical in verifying the validity of the data. Additionally, post-model training, the data underwent further verification. This included analyzing the calculated record attributions and removing recent records, as detailed in Supplementary Fig.  6 . These steps were essential to identify and mitigate any potential issues of record leakage. In the accompanying code release, we have provided the exact code used to extract and prepare the health records.

Spatial validation and data preprocessing

For model development and testing, we split the data set into 22 spatially separated partitions based on the location of the assessment center at recruitment. We analyzed the data in 22-fold nested cross-validation, setting aside one of the spatially separated partitions as a test set, aggregating the remaining partitions, and randomly selecting 10% of the aggregated data for the validation set. Within each of the 22 cross-validation loops, the individual test set (i.e., the spatially separated partition) remained untouched throughout model development, and the validation set was used to validate the fitting progress and checkpoint selection. All 22 obtained models were then evaluated on their respective test sets. We assumed missing data occurred randomly and performed multiple imputations using chained equations with gradient boosting machines 63 , 64 . Imputation models were fitted on the training sets and applied to the respective validation and test sets. Continuous variables were standardized; Categorical variables were one-hot encoded.

Development of the phenome-wide risk model

The risk model is a multi-task neural network that uses the binary representations of an individual’s prior health records before recruitment to simultaneously predict log partial hazards 65 for a set of 1883 endpoints. The model consists of three fully connected linear layers with 4096 hidden units, each with layer normalisation 66 , dropout 67 , and leaky ReLU activations. The last latent representation serves as a regulariser as it incentives the extraction of robust features for multiple diseases. For comparison, we also benchmarked the linear version of our model with 29.4 M instead of 88.4 M parameters (see Suppl. Figure  2 ). The model subsequently computes the log partial hazard (the risk state) for each endpoint with an adapted proportional hazard loss 65 , resulting in a 1,883-dimensional output representation. The individual losses are averaged and then summed to derive the final loss of the model. We subsequently tuned hyperparameters (via Bayesian Optimization) on train and validation splits over a constrained parameter space, tuning batch size, learning rate, weight decay, number of nodes in the layers of the endpoint heads, number of hidden layers, dropout rates, and size of the output vector of the shared network. The final models were trained with batch size 512 using the Adam optimiser 68 with a learning rate of 0.0006 and weight decay of 0.3, and early stopping tracking of the performance on the validation set. We implemented the model in Python 3.9 using PyTorch 1.11 69 and PyTorch-lightning 1.5.5 (for code availability, see below). The training of a single model on an NVIDIA A100 GPU node for 18 epochs required approximately 11 hours, equating to the emission of approximately 1.08 kg CO2 eq, 4.36 km driven by an average ICE car or 0.54 kgs of coal burned as calculated by the mlco2 calculator 70 . The external validation of these models, conducted within the All of Us cloud computing environment and including data preprocessing, inference, and evaluation, incurred a total compute cost of approximately 150 USD.

Downstream analysis and performance comparisons

We fitted Cox proportional hazards models 36 (CPH) to derive absolute risk predictions from the endpoint-specific risk states for the individual endpoints. For each endpoint, we developed models with distinct covariate sets: for all endpoints, we investigated age, biological sex, and the risk states from the health records. For cardiovascular endpoints, we additionally investigated predictors from established and guideline-recommended scores for the primary prevention of cardiovascular diseases, the SCORE2, ASCVD, and QRISK3. Model development was repeated independently for each assessment center thus, for each cross-validation split, models were trained on the respective train set, and checkpoints were selected on the respective validation set. For the final evaluation, test set predictions from the spatially separate recruitment centers were aggregated. Event risk rates were calculated over the full observation period. Harrell’s C-Index 71 was calculated with the lifelines package 72 by bootstrapping both the aggregated test set and individual assessment centers within ten years after recruitment to control for right-censoring. The C-Index is a measure of rank correlation that quantifies the agreement between predicted and observed outcomes. It ranges between 0.5 (no better than random prediction) to 1 (perfect prediction). Statistical inferences about model differences were based on the distribution of bootstrapped differences in the C-Index; models were considered different whenever the Bonferroni-corrected 95% CI of the difference did not overlap cross zero, to account for multiple testing. CPH models were fitted with the CoxPHFitter from the Python package lifelines 72 with default parameters and a step size of 0.5, 0.1, or 0.01 to facilitate model convergence. Confidence intervals for all statistical analyses were calculated over 1000 bootstrapping iterations.

Response to emerging health threats

We retrained our models using data limited to records until the end of December 2019, keeping the setting (in particular time zero for training) unchanged. Using these updated models, we then predicted the risk states using all data available at the end of 2019, just as the first cases of COVID-19 were reported. We then manually selected specific risk states associated with pneumonia, sepsis, and all-cause mortality to create an unweighted COVID-19 severity risk score. This risk score was subsequently tested against age for the identification of incident severe and fatal COVID-19 cases.

Independent validation in the All Of Us cohort

After mapping the linked health records from All Of Us to the OMOP vocabulary, we transferred the neural networks developed in the UK Biobank to the All Of Us research environment. We then used the models to predict the disease-specific risk states for all individuals. Subsequently, we predicted absolute risks with the CPH models developed in the UK Biobank. Finally, we calculated the mean of the predictions from the models for each individual and disease. For baseline comparison with Age and Sex, we fitted new CPH models in the All Of Us cohort.

Calculation of record attributions

To determine which records are most important on an individual level, we calculated attributions for the selection of 24 endpoints based on Shapley values. For computational efficiency, we approximated Shapley values via sampling for only 17,236 individuals unseen to the model during development 43 . Please refer to Supplementary Data  9 for the aggregated attributions from individuals without prior events. Shapley values in the table are provided in two forms: averaged (so-called local attributions to quantify importance for affected individuals) and summed (global attributions to quantify importance for population ranking). The average Shapley attributions, presented in the main text and figures, closely reflect our understanding of importance for affected individuals.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

UK Biobank data, including all linked routine health records, are publicly available to bona fide researchers upon application at http://www.ukbiobank.ac.uk/using-the-resource/ . In this study, primary care data was used following the COPI regulations. The All Of Us cohort data were provided by the All Of Us Research Program by permission that can be sought by scientists and the public alike. Currently, however, data access requires affiliation with a US institution. All patient data used throughout this study has been subject to patient consent as covered by the UK Biobank and All Of Us. Detailed information on the predictors and endpoints is presented in Supplementary Data  1 - 3 . Source data are provided with this paper.

Code availability

All code developed and used throughout this study has been made open source and is available on GitHub. The code to train the medical history model can be found here: github.com/nebw/medhist, while the code to run analysis on trained models can be found here: github.com/JakobSteinfeldt/MedicalHistoryPhenomeWide.

Sindi, S. et al. The CAIDE Dementia Risk Score App: The development of an evidence-based mobile application to predict the risk of dementia. Alzheimers Dement 1 , 328–333 (2015).

Google Scholar  

Lindström, J. & Tuomilehto, J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diab. Care 26 , 725–731 (2003).

Article   Google Scholar  

Goff, D. C. et al. 2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk. Circulation 129 , S49–S73 (2014).

Article   PubMed   Google Scholar  

Hippisley-Cox, J., Coupland, C. & Brindle, P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ 357 , j2099 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Steyerberg, E. W. et al. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 10 , e1001381 (2013).

Hampton, J. R., Harrison, M. J., Mitchell, J. R., Prichard, J. S. & Seymour, C. Relative contributions of history-taking, physical examination, and laboratory investigation to diagnosis and management of medical outpatients. Br. Med J. 2 , 486–489 (1975).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Danish eHealth Portal. Danish eHealth Portal. Danish eHealth Portal. 2001. https://www.sundhed.dk/borger/service/om-sundheddk/om-organisationen/ehealth-in-denmark/background/ .

e-Health Record. e-Health Record. e-Health Record. 2005. https://e-estonia.com/solutions/healthcare/e-health-records/ .

Clalit Research Institute. Clalit Health Services. Clalit Health Services. 2010. http://clalitresearch.org/about-us/our-data/ (accessed 2010).

National Electronic Health Record. National Electronic Health Record. National Electronic Health Record. 2011. https://www.ihis.com.sg/nehr/about-nehr .

My Health Record. My Health Record. My Health Record. 2016. https://www.myhealthrecord.gov.au/ .

Wood, A. et al. Linked electronic health records for research on a nationwide cohort of more than 54 million people in England: data resource. BMJ 373 , n826 (2021).

Rush, R. Taking Note. N. Engl. J. Med 381 , 9 (2019).

Tsang, G., Zhou, S.-M. & Xie, X. Modeling Large Sparse Data for Feature Selection: Hospital Admission Predictions of the Dementia Patients Using Primary Care Electronic Health Records. IEEE J. Transl. Eng. Health Med 9 , 3000113 (2021).

Langham J. et al. Predicting risk of dementia with machine learning and survival models using routine primary care records. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2021: 3036–3042.

Rajkomar, A. et al. Scalable and accurate deep learning with electronic health records. npj Digital Med. 1 , 1–10 (2018).

Appelbaum, L. et al. Development and validation of a pancreatic cancer risk model for the general population using electronic health records: An observational study. Eur. J. Cancer 143 , 19–30 (2021).

Kronzer, V. L. et al. Investigating the impact of disease and health record duration on the eMERGE algorithm for rheumatoid arthritis. J. Am. Med Inf. Assoc. 27 , 601–605 (2020).

Sekelj, S. et al. Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study. Eur. J. Prev. Cardiol. 28 , 598–605 (2021).

Miotto, R., Li, L., Kidd, B. A. & Dudley, J. T. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Sci. Rep. 6 , 26094 (2016).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Estiri, H. et al. Predicting COVID-19 mortality with electronic medical records. NPJ Digit Med 4 , 15 (2021).

Wu J., Nadarajah R., Raveendra K., Cowan J. C., & Gale C. P. FIND-AF: a widely applicable artificial intelligence algorithm to target systematic screening for atrial fibrillation in older individuals through primary care electronic health records. Europace 2022; 24 . https://doi.org/10.1093/europace/euac053.565 .

Bagheri A. et al. Multimodal Learning for Cardiovascular Risk Prediction using EHR Data. arXiv [cs.LG]. 2020; published online Aug 27. http://arxiv.org/abs/2008.11979 .

Ben Miled, Z. et al. Predicting dementia with routine care EMR data. Artif. Intell. Med 102 , 101771 (2020).

Zhao, J. et al. Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction. Sci. Rep. 9 , 717 (2019).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Jin, B. et al. Predicting the Risk of Heart Failure With EHR Sequential Data Modeling. IEEE Access Undefined 6 , 9256–9261 (2018).

Hill, N. R. et al. Predicting atrial fibrillation in primary care using machine learning. PLoS One 14 , e0224582 (2019).

Tiwari, P. et al. Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation. JAMA Netw. Open 3 , e1919396 (2020).

Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31 , 1102–1110 (2013).

Bush, W. S., Oetjens, M. T. & Crawford, D. C. Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat. Rev. Genet 17 , 129–145 (2016).

Article   CAS   PubMed   Google Scholar  

Zhang, Y. et al. High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP). Nat. Protoc. 14 , 3426–3444 (2019).

Zheng, N. S. et al. PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records. J. Am. Med Inf. Assoc. 27 , 1675–1687 (2020).

Rasmy, L., Xiang, Y., Xie, Z., Tao, C. & Zhi, D. Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. npj Digital Med. 4 , 1–13 (2021).

Li, Y. et al. BEHRT: Transformer for Electronic Health Records. Sci. Rep. 10 , 1–12 (2020).

ADS   Google Scholar  

Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562 , 203–209 (2018).

Cox, D. R. Regression Models and Life-Tables. J. R. Stat. Soc. Ser. B Stat. Methodol. 34 , 187–202 (1972).

Article   MathSciNet   Google Scholar  

All of Us Research Program Investigators, Denny, J. C. et al. The ‘All of Us’ Research Program. N. Engl. J. Med 381 , 668–676 (2019).

Sudlow, C. et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med 12 , e1001779 (2015).

Wu, P. et al. Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation. JMIR Med Inf. 7 , e14325 (2019).

Ramirez, A. H. et al. The All of Us Research Program: Data quality, utility, and diversity. Patterns (N. Y) 3 , 100570 (2022).

Martinez, F. J. et al. A New Approach for Identifying Patients with Undiagnosed Chronic Obstructive Pulmonary Disease. Am. J. Respir. Crit. Care Med 195 , 748–756 (2017).

Charlson, M. E., Pompei, P., Ales, K. L. & MacKenzie, C. R. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J. Chronic Dis. 40 , 373–383 (1987).

Castro, J., Gómez, D. & Tejada, J. Polynomial calculation of the Shapley value based on sampling. Comput Oper. Res 36 , 1726–1730 (2009).

Finlayson, S. G. et al. The Clinician and Dataset Shift in Artificial Intelligence. N. Engl. J. Med 385 , 283–286 (2021).

Wong, A. et al. External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients. JAMA Intern Med 181 , 1065–1070 (2021).

Guo, L. L. et al. Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine. Sci. Rep. 12 , 2726 (2022).

National Institute for health and Care Excellence (NICE). Cardiovascular disease: risk assessment and reduction, including lipid modification. 2014; published online July 18. https://www.nice.org.uk/guidance/cg181 (accessed Sept 16, 2022).

SCORE2 working group and ESC Cardiovascular risk collaboration. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur. Heart J. 42 , 2439–2454 (2021).

Collins, R. et al. Interpretation of the evidence for the efficacy and safety of statin therapy. Lancet 388 , 2532–2561 (2016).

Ference, B. A. et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur. Heart J. 38 , 2459–2472 (2017).

Vaduganathan, M. et al. Estimating lifetime benefits of comprehensive disease-modifying pharmacological therapies in patients with heart failure with reduced ejection fraction: a comparative analysis of three randomised controlled trials. Lancet 396 , 121–128 (2020).

Adelson, K. et al. Standardized Criteria for Palliative Care Consultation on a Solid Tumor Oncology Service Reduces Downstream Health Care Use. J. Oncol. Pr. 13 , e431–e440 (2017).

Weissman, D. E. & Meier, D. E. Identifying patients in need of a palliative care assessment in the hospital setting: a consensus report from the Center to Advance Palliative Care. J. Palliat. Med 14 , 17–23 (2011).

Centeno, C. & Arias-Casais, N. Global palliative care: from need to action. Lancet Glob. Health 7 , e815–e816 (2019).

de Lemos, J. A. et al. Multimodality Strategy for Cardiovascular Risk Assessment: Performance in 2 Population-Based Cohorts. Circulation 135 , 2119–2132 (2017).

Steinfeldt, J. et al. Neural network-based integration of polygenic and clinical information: development and validation of a prediction model for 10-year risk of major adverse cardiac events in the UK Biobank cohort. Lancet Digit Health 4 , e84–e94 (2022).

Buergel T., et al. Metabolomic profiles predict individual multidisease outcomes. Nat. Med. https://doi.org/10.1038/s41591-022-01980-3 2022.

Vayena, E. Value from health data: European opportunity to catalyse progress in digital health. Lancet 397 , 652–653 (2021).

Denaxas, S. et al. A semi-supervised approach for rapidly creating clinical biomarker phenotypes in the UK Biobank using different primary care EHR and clinical terminology systems. JAMIA Open 3 , 545–556 (2020).

Fry, A. et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am. J. Epidemiol. 186 , 1026–1034 (2017).

Wei, W.-Q. et al. Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record. PLoS One 12 , e0175508 (2017).

Moons, K. G. M. et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann. Intern. Med. 162 , W1–W73 (2015).

Stekhoven, D. J. & Bühlmann, P. MissForest-non-parametric missing value imputation for mixed-type data. Bioinformatics 28 , 112–118 (2012).

miceforest. PyPI. https://pypi.org/project/miceforest/ (accessed July 6, 2022).

Katzman J. L. et al. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC. Med. Res. Methodol . 18 , 24 (2018).

Ba J. L., Kiros J. R. & Hinton G. E. Layer Normalization. arXiv [stat.ML]. 2016; published online July 21. http://arxiv.org/abs/1607.06450 .

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. J. Mach. Learn Res 15 , 1929–1958 (2014).

MathSciNet   Google Scholar  

Kingma D. P. & Ba J. L. Adam: a Method for stochastic optimization. In International Conference on Learning Representations 2015 (ICLR, 2015).

Paszke, A. et al. Automatic differentiation in PyTorch. Adv. Neural Inf. Process. Syst. 30 , 1–4 (2017).

Machine Learning CO2 impact calculator. https://mlco2.github.io/impact/ (accessed May 10, 2023).

Harrell, F. E. et al. Evaluating the yield of medical tests. JAMA 247 , 2543–2546 (1982).

lifelines 0.25.8. 2021. https://lifelines.readthedocs.io/en/latest/ (accessed Feb 3, 2021).

How does All of Us assess diversity? What communities does All of Us consider ‘underrepresented in biomedical research?’ https://www.researchallofus.org/faq/how-does-all-of-us-assess-diversity-what-communities-does-all-of-us-consider-underrepresented-in-biomedical-research/ (accessed May 5, 2023).

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Acknowledgements

We would like to acknowledge the support of the UK Biobank and the All of Us Research Program in providing access to their respective datasets. This research has been conducted using data from the UK Biobank (application number 51157) and the All of Us Research Program (by S.H. UserID 5703). Both studies have received ethical approval from their respective institutional review boards and have obtained informed consent from participants. We are grateful to the participants who generously contributed their time and data to make this research possible. This project has been funded by the Charité - Universitätsmedizin Berlin and the Einstein Foundation Berlin through the Einstein BIH Visiting Fellowship awarded to J.D. The study has been supported by the BMBF-funded Medical Informatics Initiative (HiGHmed, 01ZZ1802A − 01ZZ1802Z) and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 437531118 – SFB 1470. SD is supported by a) the BHF Data Science Centre led by HDR UK (grant SP/19/3/34678), b) BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement 116074, c) the NIHR Biomedical Research Centre at University College London Hospital NHS Trust (UCLH BRC), d) a BHF Accelerator Award (AA/18/6/24223), e) the CVD-COVID-UK/COVID-IMPACT consortium and f) the Multimorbidity Mechanism and Therapeutic Research Collaborative (MMTRC, grant number MR/V033867/1). HH is supported by Health Data Research UK and the National Institute for Health Research, Biomedical Research Centre at University College London Hospitals.

Open Access funding enabled and organized by Projekt DEAL.

Author information

These authors contributed equally: Jakob Steinfeldt, Benjamin Wild, Thore Buergel.

These authors jointly supervised this work: Ulf Landmesser, John Deanfield, Roland Eils.

Authors and Affiliations

Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany

Jakob Steinfeldt & Ulf Landmesser

Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Klinik/Centrum, Charitéplatz 1, 10117, Berlin, Germany

Computational Medicine, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany

Jakob Steinfeldt, Maik Pietzner & Claudia Langenberg

Friede Springer Cardiovascular Prevention Center@Charite, Charite - University Medicine Berlin, Berlin, Germany

Institute of Cardiovascular Sciences, University College London, London, UK

Jakob Steinfeldt, Thore Buergel & John Deanfield

Center for Digital Health, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany

Benjamin Wild, Thore Buergel, Julius Upmeier zu Belzen & Roland Eils

MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK

Maik Pietzner & Claudia Langenberg

Precision Health University Research Institute, Queen Mary University of London and Barts NHS Trust, London, UK

Institute of Health Informatics, University College London, London, UK

Andre Vauvelle, Spiros Denaxas & Harry Hemingway

Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Massachusetts, USA

Stefan Hegselmann

Pattern Recognition and Image Analysis Lab, University of Münster, Münster, Germany

British Heart Foundation Data Science Centre, London, UK

Spiros Denaxas

Health Data Research UK, London, UK

Spiros Denaxas & Harry Hemingway

National Institute for Health Research, Biomedical Research Centre at University College London Hospitals National Institute for Health Research, Biomedical Research Centre, London, UK

Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany

Ulf Landmesser

DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Berlin, Germany

Health Data Science Unit, Heidelberg University Hospital and BioQuant, Heidelberg, Germany

Roland Eils

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Contributions

J.S., B.W., T.B., M.P., H.H., C.L., U.L., J.D., and R.E. conceived and designed the project. J.S., B.W., and T.B. implemented models, conducted experiments, and performed data analysis. J.U. and A.V. supported the analysis. S.H. performed the external validation. M.P., S.D., H.H., and C.L. provided methodological support and contributed to the discussion of the results. J.S., B.W., T.B., U.L., J.D., and R.E. wrote and prepared the manuscript. All authors read, revised, and approved the manuscript.

Corresponding author

Correspondence to Roland Eils .

Ethics declarations

Competing interests.

U.L. received research grants to the institution from Abbott, Amgen, Bayer and Novartis. J.D. received honoraria from Amgen, Boehringer Ingelheim, Merck, Pfizer, Aegerion, Novartis, Sanofi, Takeda, Novo Nordisk, Bayer, and is a Trustee of Our Future Health. R.E. received honoraria from Sanofi and consulting fees from Boehringer Ingelheim. All other authors do declare no competing interests.

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Supplementary information

Supplementary information, description of additional supplementary files, supplementary data 1: endpoints in this study, supplementary data 2: medical history predictors in this study, supplementary data 3: reference predictors in this study, supplementary data 4: incident event stratification, supplementary data 5: discriminative performance of the medical history scores, supplementary data 6: hazard ratios of the medical history scores, 41467_2024_48568_moesm9_esm.xlsx.

Supplementary Data 7: Discriminative Performance of the Medical History Scores to compared to established scores for the Primary Prevention of Cardiovascular Disease

Supplementary Data 8: Discriminative performance in the All of Us cohort

Supplementary data 9: feature attributions for 24 selected endpoints, reporting summary, peer review file, source data, rights and permissions.

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Steinfeldt, J., Wild, B., Buergel, T. et al. Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats. Nat Commun 15 , 4257 (2024). https://doi.org/10.1038/s41467-024-48568-8

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