problem solving in clinical medicine

• - Google Chrome

Intended for healthcare professionals

• Access provided by Google Indexer
• My email alerts
• BMA member login
• Username * Password * Forgot your log in details? Need to activate BMA Member Log In Log in via OpenAthens Log in via your institution

Search form

• Advanced search
• Search responses
• Search blogs
• Clinical problem...

Clinical problem solving and diagnostic decision making: selective review of the cognitive literature

• Related content
• Peer review

This article has a correction. Please see:

• Clinical problem solving and diagnostic decision making: selective review of the cognitive literature - November 02, 2006
• Arthur S Elstein , professor ( aelstein{at}uic.edu ) ,
• Alan Schwarz , assistant professor of clinical decision making.
• Department of Medical Education, University of Illinois College of Medicine, Chicago, IL 60612-7309, USA
• Correspondence to: A S Elstein

This is the fourth in a series of five articles

This article reviews our current understanding of the cognitive processes involved in diagnostic reasoning in clinical medicine. It describes and analyses the psychological processes employed in identifying and solving diagnostic problems and reviews errors and pitfalls in diagnostic reasoning in the light of two particularly influential approaches: problem solving 1 , 2 , 3 and decision making. 4 , 5 , 6 , 7 , 8 Problem solving research was initially aimed at describing reasoning by expert physicians, to improve instruction of medical students and house officers. Psychological decision research has been influenced from the start by statistical models of reasoning under uncertainty, and has concentrated on identifying departures from these standards.

Summary points

Problem solving and decision making are two paradigms for psychological research on clinical reasoning, each with its own assumptions and methods

The choice of strategy for diagnostic problem solving depends on the perceived difficulty of the case and on knowledge of content as well as strategy

Final conclusions should depend both on prior belief and strength of the evidence

Conclusions reached by Bayes's theorem and clinical intuition may conflict

Because of cognitive limitations, systematic biases and errors result from employing simpler rather than more complex cognitive strategies

Evidence based medicine applies decision theory to clinical diagnosis

Problem solving

Diagnosis as selecting a hypothesis.

The earliest psychological formulation viewed diagnostic reasoning as a process of testing hypotheses. Solutions to difficult diagnostic problems were found by generating a limited number of hypotheses early in the diagnostic process and using them to guide subsequent collection of data. 1 Each hypothesis can be used to predict what additional findings ought to be present if it were true, and the diagnostic process is a guided search for these findings. Experienced physicians form hypotheses and their diagnostic plan rapidly, and the quality of their hypotheses is higher than that of novices. Novices struggle to develop a plan and some have difficulty moving beyond collection of data to considering possibilities.

It is possible to collect data thoroughly but nevertheless to ignore, to misunderstand, or to misinterpret some findings, but also possible for a clinician to be too economical in collecting data and yet to interpret accurately what is available. Accuracy and thoroughness are analytically separable.

Pattern recognition or categorisation

Expertise in problem solving varies greatly between individual clinicians and is highly dependent on the clinician's mastery of the particular domain. 9 This finding challenges the hypothetico-deductive model of clinical reasoning, since both successful and unsuccessful diagnosticians use hypothesis testing. It appears that diagnostic accuracy does not depend as much on strategy as on mastery of content. Further, the clinical reasoning of experts in familiar situations frequently does not involve explicit testing of hypotheses. 3 10 , 11 , 12 Their speed, efficiency, and accuracy suggest that they may not even use the same reasoning processes as novices. 11 It is likely that experienced physicians use a hypothetico-deductive strategy only with difficult cases and that clinical reasoning is more a matter of pattern recognition or direct automatic retrieval. What are the patterns? What is retrieved? These questions signal a shift from the study of judgment to the study of the organisation and retrieval of memories.

Problem solving strategies

Hypothesis testing

Pattern recognition (categorisation)

By specific instances

By general prototypes

Viewing the process of diagnosis assigning a case to a category brings some other issues into clearer view. How is a new case categorised? Two competing answers to this question have been put forward and research evidence supports both. Category assignment can be based on matching the case to a specific instance (“instance based” or “exemplar based” recognition) or to a more abstract prototype. In the former, a new case is categorised by its resemblance to memories of instances previously seen. 3 11 This model is supported by the fact that clinical diagnosis is strongly affected by context—for example, the location of a skin rash on the body—even when the context ought to be irrelevant. 12

The prototype model holds that clinical experience facilitates the construction of mental models, abstractions, or prototypes. 2 13 Several characteristics of experts support this view—for instance, they can better identify the additional findings needed to complete a clinical picture and relate the findings to an overall concept of the case. These features suggest that better diagnosticians have constructed more diversified and abstract sets of semantic relations, a network of links between clinical features and diagnostic categories. 14

The controversy about the methods used in diagnostic reasoning can be resolved by recognising that clinicians approach problems flexibly; the method they select depends upon the perceived characteristics of the problem. Easy cases can be solved by pattern recognition: difficult cases need systematic generation and testing of hypotheses. Whether a diagnostic problem is easy or difficult is a function of the knowledge and experience of the clinician.

The strategies reviewed are neither proof against error nor always consistent with statistical rules of inference. Errors that can occur in difficult cases in internal medicine include failure to generate the correct hypothesis; misperception or misreading the evidence, especially visual cues; and misinterpretations of the evidence. 15 16 Many diagnostic problems are so complex that the correct solution is not contained in the initial set of hypotheses. Restructuring and reformulating should occur as data are obtained and the clinical picture evolves. However, a clinician may quickly become psychologically committed to a particular hypothesis, making it more difficult to restructure the problem.

Decision making

Diagnosis as opinion revision.

From the point of view of decision theory, reaching a diagnosis means updating opinion with imperfect information (the clinical evidence). 8 17 The standard rule for this task is Bayes's theorem. The pretest probability is either the known prevalence of the disease or the clinician's subjective impression of the probability of disease before new information is acquired. The post-test probability, the probability of disease given new information, is a function of two variables, pretest probability and the strength of the evidence, measured by a “likelihood ratio.”

Bayes's theorem tells us how we should reason, but it does not claim to describe how opinions are revised. In our experience, clinicians trained in methods of evidence based medicine are more likely than untrained clinicians to use a Bayesian approach to interpreting findings. 18 Nevertheless, probably only a minority of clinicians use it in daily practice and informal methods of opinion revision still predominate. Bayes's theorem directs attention to two major classes of errors in clinical reasoning: in the assessment of either pretest probability or the strength of the evidence. The psychological study of diagnostic reasoning from this viewpoint has focused on errors in both components, and on the simplifying rules or heuristics that replace more complex procedures. Consequently, this approach has become widely known as “heuristics and biases.” 4 19

Errors in estimation of probability

Availability —People are apt to overestimate the frequency of vivid or easily recalled events and to underestimate the frequency of events that are either very ordinary or difficult to recall. Diseases or injuries that receive considerable media attention are often thought of as occurring more commonly than they actually do. This psychological principle is exemplified clinically in the overemphasis of rare conditions, because unusual cases are more memorable than routine problems.

Representativeness —Representativeness refers to estimating the probability of disease by judging how similar a case is to a diagnostic category or prototype. It can lead to overestimation of probability either by causing confusion of post-test probability with test sensitivity or by leading to neglect of base rates and implicitly considering all hypotheses equally likely. This is an error, because if a case resembles disease A and disease B equally, and A is much more common than B, then the case is more likely to be an instance of A. Representativeness is associated with the “conjunction fallacy”—incorrectly concluding that the probability of a joint event (such as the combination of findings to form a typical clinical picture) is greater than the probability of any one of these events alone.

Heuristics and biases

Availability

Representativeness

Probability transformations

Effect of description detail

Conservatism

Anchoring and adjustment

Order effects

Decision theory assumes that in psychological processing of probabilities, they are not transformed from the ordinary probability scale. Prospect theory was formulated as a descriptive account of choices involving gambling on two outcomes, 20 and cumulative prospect theory extends the theory to cases with multiple outcomes. 21 Both prospect theory and cumulative prospect theory propose that, in decision making, small probabilities are overweighted and large probabilities underweighted, contrary to the assumption of standard decision theory. This “compression” of the probability scale explains why the difference between 99% and 100% is psychologically much greater than the difference between, say, 60% and 61%. 22

Support theory

Support theory proposes that the subjective probability of an event is inappropriately influenced by how detailed the description is. More explicit descriptions yield higher probability estimates than compact, condensed descriptions, even when the two refer to exactly the same events. Clinically, support theory predicts that a longer, more detailed case description will be assigned a higher subjective probability of the index disease than a brief abstract of the same case, even if they contain the same information about that disease. Thus, subjective assessments of events, while often necessary in clinical practice, can be affected by factors unrelated to true prevalence. 23

Errors in revision of probability

In clinical case discussions, data are presented sequentially, and diagnostic probabilities are not revised as much as is implied by Bayes's theorem 8 ; this phenomenon is called conservatism. One explanation is that diagnostic opinions are revised up or down from an initial anchor, which is either given in the problem or subjectively formed. Final opinions are sensitive to the starting point (the “anchor”), and the shift (“adjustment”) from it is typically insufficient. 4 Both biases will lead to collecting more information than is necessary to reach a desired level of diagnostic certainty.

It is difficult for everyday judgment to keep separate accounts of the probability of a disease and the benefits that accrue from detecting it. Probability revision errors that are systematically linked to the perceived cost of mistakes show the difficulties experienced in separating assessments of probability from values, as required by standard decision theory. There is a tendency to overestimate the probability of more serious but treatable diseases, because a clinician would hate to miss one. 24

Bayes's theorem implies that clinicians given identical information should reach the same diagnostic opinion, regardless of the order in which information is presented. However, final opinions are also affected by the order of presentation of information. Information presented later in a case is given more weight than information presented earlier. 25

Other errors identified in data interpretation include simplifying a diagnostic problem by interpreting findings as consistent with a single hypothesis, forgetting facts inconsistent with a favoured hypothesis, overemphasising positive findings, and discounting negative findings. From a Bayesian standpoint, these are all errors in assessing the diagnostic value of clinical evidence—that is, errors in implicit likelihood ratios.

Featured Clinical Reviews

• Screening for Atrial Fibrillation: US Preventive Services Task Force Recommendation Statement JAMA Recommendation Statement January 25, 2022
• Evaluating the Patient With a Pulmonary Nodule: A Review JAMA Review January 18, 2022
• Download PDF
• Share X Facebook Email LinkedIn
• Permissions

Problem Solving in Clinical Medicine: From Data to Diagnosis

New York University School of Medicine New York

This article is only available in the PDF format. Download the PDF to view the article, as well as its associated figures and tables.

This volume is specifically designed for the medical student who is at or just beyond the junction of preclinical and clinical experiences. There are 31 contributing physicians, 25 of whom are at the University of Texas Health Science Centers at San Antonio.

The book is written and guided by an obviously experienced senior physician, whose goal is to enable students "to enter the physician's mind during the gathering of data and to learn why he pursues certain pathways in his quest for solutions." The volume is obviously more than that, but, to this reviewer, that is its most notable message.

The first half is effectively conceptual and methodological, reminding medical students that they can, in fact, utilize their broad knowledge in the care of actual patients. The second half provides specific examples of "problem solving in action." Students might enjoy looking at specific case studies in this section to determine

Wessler S. Problem Solving in Clinical Medicine: From Data to Diagnosis. JAMA. 1985;254(21):3109. doi:10.1001/jama.1985.03360210125049

Manage citations:

© 2024

Artificial Intelligence Resource Center

Cardiology in JAMA : Read the Latest

Browse and subscribe to JAMA Network podcasts!

Others Also Liked

Select your interests.

Customize your JAMA Network experience by selecting one or more topics from the list below.

• Academic Medicine
• Acid Base, Electrolytes, Fluids
• Allergy and Clinical Immunology
• American Indian or Alaska Natives
• Anesthesiology
• Anticoagulation
• Art and Images in Psychiatry
• Artificial Intelligence
• Assisted Reproduction
• Bleeding and Transfusion
• Caring for the Critically Ill Patient
• Challenges in Clinical Electrocardiography
• Climate and Health
• Climate Change
• Clinical Challenge
• Clinical Decision Support
• Clinical Implications of Basic Neuroscience
• Clinical Pharmacy and Pharmacology
• Complementary and Alternative Medicine
• Consensus Statements
• Coronavirus (COVID-19)
• Critical Care Medicine
• Cultural Competency
• Dental Medicine
• Dermatology
• Diabetes and Endocrinology
• Diagnostic Test Interpretation
• Drug Development
• Electronic Health Records
• Emergency Medicine
• End of Life, Hospice, Palliative Care
• Environmental Health
• Equity, Diversity, and Inclusion
• Facial Plastic Surgery
• Gastroenterology and Hepatology
• Genetics and Genomics
• Genomics and Precision Health
• Global Health
• Guide to Statistics and Methods
• Hair Disorders
• Health Care Delivery Models
• Health Care Economics, Insurance, Payment
• Health Care Quality
• Health Care Reform
• Health Care Safety
• Health Care Workforce
• Health Disparities
• Health Inequities
• Health Policy
• Health Systems Science
• History of Medicine
• Hypertension
• Images in Neurology
• Implementation Science
• Infectious Diseases
• Innovations in Health Care Delivery
• JAMA Infographic
• Law and Medicine
• Leading Change
• Less is More
• LGBTQIA Medicine
• Lifestyle Behaviors
• Medical Coding
• Medical Devices and Equipment
• Medical Education
• Medical Education and Training
• Medical Journals and Publishing
• Mobile Health and Telemedicine
• Narrative Medicine
• Neuroscience and Psychiatry
• Notable Notes
• Nutrition, Obesity, Exercise
• Obstetrics and Gynecology
• Occupational Health
• Ophthalmology
• Orthopedics
• Otolaryngology
• Pain Medicine
• Palliative Care
• Pathology and Laboratory Medicine
• Patient Care
• Patient Information
• Performance Improvement
• Performance Measures
• Perioperative Care and Consultation
• Pharmacoeconomics
• Pharmacoepidemiology
• Pharmacogenetics
• Pharmacy and Clinical Pharmacology
• Physical Medicine and Rehabilitation
• Physical Therapy
• Physician Leadership
• Population Health
• Primary Care
• Professional Well-being
• Professionalism
• Psychiatry and Behavioral Health
• Public Health
• Pulmonary Medicine
• Regulatory Agencies
• Reproductive Health
• Research, Methods, Statistics
• Resuscitation
• Rheumatology
• Risk Management
• Scientific Discovery and the Future of Medicine
• Shared Decision Making and Communication
• Sleep Medicine
• Sports Medicine
• Stem Cell Transplantation
• Substance Use and Addiction Medicine
• Surgical Innovation
• Surgical Pearls
• Teachable Moment
• Technology and Finance
• The Art of JAMA
• The Arts and Medicine
• The Rational Clinical Examination
• Tobacco and e-Cigarettes
• Translational Medicine
• Trauma and Injury
• Treatment Adherence
• Ultrasonography
• Users' Guide to the Medical Literature
• Vaccination
• Venous Thromboembolism
• Veterans Health
• Women's Health
• Workflow and Process
• Wound Care, Infection, Healing
• Register for email alerts with links to free full-text articles
• Access PDFs of free articles
• Manage your interests
• Save searches and receive search alerts

We will keep fighting for all libraries - stand with us!

Internet Archive Audio

• This Just In
• Grateful Dead
• Old Time Radio
• 78 RPMs and Cylinder Recordings
• Audio Books & Poetry
• Computers, Technology and Science
• Music, Arts & Culture
• News & Public Affairs
• Spirituality & Religion
• Radio News Archive

• Flickr Commons
• Occupy Wall Street Flickr
• NASA Images
• Solar System Collection
• Ames Research Center

• All Software
• Old School Emulation
• MS-DOS Games
• Historical Software
• Classic PC Games
• Software Library
• Kodi Archive and Support File
• Vintage Software
• CD-ROM Software
• CD-ROM Software Library
• Software Sites
• Tucows Software Library
• Shareware CD-ROMs
• Software Capsules Compilation
• CD-ROM Images
• ZX Spectrum
• DOOM Level CD

• Smithsonian Libraries
• FEDLINK (US)
• Lincoln Collection
• American Libraries
• Canadian Libraries
• Universal Library
• Project Gutenberg
• Children's Library
• Biodiversity Heritage Library
• Books by Language
• Additional Collections

• Prelinger Archives
• Democracy Now!
• Occupy Wall Street
• TV NSA Clip Library
• Animation & Cartoons
• Arts & Music
• Computers & Technology
• Cultural & Academic Films
• Ephemeral Films
• Sports Videos
• Videogame Videos
• Youth Media

Search the history of over 866 billion web pages on the Internet.

Mobile Apps

• Wayback Machine (iOS)
• Wayback Machine (Android)

Browser Extensions

Archive-it subscription.

• Explore the Collections
• Build Collections

Save Page Now

Capture a web page as it appears now for use as a trusted citation in the future.

Please enter a valid web address

• Donate Donate icon An illustration of a heart shape

Problem solving in clinical medicine : from data to diagnosis

Bookreader item preview, share or embed this item, flag this item for.

• Graphic Violence
• Explicit Sexual Content
• Hate Speech
• Misinformation/Disinformation
• Marketing/Phishing/Advertising
• Misleading/Inaccurate/Missing Metadata

plus-circle Add Review comment Reviews

235 Previews

Better World Books

DOWNLOAD OPTIONS

No suitable files to display here.

EPUB and PDF access not available for this item.

IN COLLECTIONS

Uploaded by Alethea Bowser on December 8, 2011

SIMILAR ITEMS (based on metadata)

• Medical Books

Buy new: .savingPriceOverride { color:#CC0C39!important; font-weight: 300!important; } .reinventMobileHeaderPrice { font-weight: 400; } #apex_offerDisplay_mobile_feature_div .reinventPriceSavingsPercentageMargin, #apex_offerDisplay_mobile_feature_div .reinventPricePriceToPayMargin { margin-right: 4px; } $41.99 $ 41 . 99 FREE delivery May 8 - 10 Ships from: The Art of Savings Sold by: The Art of Savings

Save with used - good .savingpriceoverride { color:#cc0c39important; font-weight: 300important; } .reinventmobileheaderprice { font-weight: 400; } #apex_offerdisplay_mobile_feature_div .reinventpricesavingspercentagemargin, #apex_offerdisplay_mobile_feature_div .reinventpricepricetopaymargin { margin-right: 4px; } $34.67 $ 34 . 67 free delivery wednesday, may 8 on orders shipped by amazon over $35 ships from: amazon sold by: azbook73, return this item for free.

Free returns are available for the shipping address you chose. You can return the item for any reason in new and unused condition: no shipping charges

• Go to your orders and start the return
• Select the return method

Download the free Kindle app and start reading Kindle books instantly on your smartphone, tablet, or computer - no Kindle device required .

Read instantly on your browser with Kindle for Web.

Using your mobile phone camera - scan the code below and download the Kindle app.

Image Unavailable

• To view this video download Flash Player

Follow the author

Problem Solving in Clinical Medicine: From Data to Diagnosis Subsequent Edition

There is a newer edition of this item:, purchase options and add-ons.

• ISBN-10 0683022520
• ISBN-13 978-0683022520
• Edition Subsequent
• Publisher Williams & Wilkins
• Publication date January 1, 1985
• Language English
• Dimensions 7.25 x 1.5 x 1.5 inches
• Print length 636 pages
• See all details

Customers who bought this item also bought

Product details

• Publisher ‏ : ‎ Williams & Wilkins; Subsequent edition (January 1, 1985)
• Language ‏ : ‎ English
• Hardcover ‏ : ‎ 636 pages
• ISBN-10 ‏ : ‎ 0683022520
• ISBN-13 ‏ : ‎ 978-0683022520
• Item Weight ‏ : ‎ 2.72 pounds
• Dimensions ‏ : ‎ 7.25 x 1.5 x 1.5 inches
• #410 in Medical Diagnosis (Books)
• #241,538 in Unknown

About the author

Paul cutler.

Discover more of the author’s books, see similar authors, read author blogs and more

Customer reviews

Customer Reviews, including Product Star Ratings help customers to learn more about the product and decide whether it is the right product for them.

To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzed reviews to verify trustworthiness.

• Sort reviews by Top reviews Most recent Top reviews

Top reviews from the United States

There was a problem filtering reviews right now. please try again later..

Top reviews from other countries

• Amazon Newsletter
• About Amazon
• Accessibility
• Sustainability
• Press Center
• Investor Relations
• Amazon Devices
• Amazon Science
• Sell on Amazon
• Sell apps on Amazon
• Supply to Amazon
• Protect & Build Your Brand
• Become an Affiliate
• Become a Delivery Driver
• Start a Package Delivery Business
• Advertise Your Products
• Self-Publish with Us
• Become an Amazon Hub Partner
• › See More Ways to Make Money
• Amazon Visa
• Amazon Store Card
• Amazon Secured Card
• Amazon Business Card
• Shop with Points
• Credit Card Marketplace
• Reload Your Balance
• Amazon Currency Converter
• Your Account
• Your Orders
• Shipping Rates & Policies
• Amazon Prime
• Returns & Replacements
• Manage Your Content and Devices
• Recalls and Product Safety Alerts
• Conditions of Use
• Privacy Notice
• Consumer Health Data Privacy Disclosure
• Your Ads Privacy Choices

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Perspective
• Published: 19 April 2024

Causal machine learning for predicting treatment outcomes

• Stefan Feuerriegel   ORCID: orcid.org/0000-0001-7856-8729 1 , 2 ,
• Dennis Frauen 1 , 2 ,
• Valentyn Melnychuk 1 , 2 ,
• Jonas Schweisthal   ORCID: orcid.org/0000-0003-3725-3821 1 , 2 ,
• Konstantin Hess   ORCID: orcid.org/0009-0003-8552-6588 1 , 2 ,
• Alicia Curth 3 ,
• Stefan Bauer   ORCID: orcid.org/0000-0003-1712-060X 4 , 5 ,
• Niki Kilbertus   ORCID: orcid.org/0000-0001-8718-4305 2 , 4 , 5 ,
• Isaac S. Kohane 6 &
• Mihaela van der Schaar 7 , 8  

Nature Medicine volume  30 ,  pages 958–968 ( 2024 ) Cite this article

8695 Accesses

141 Altmetric

Metrics details

• Medical research
• Predictive markers
• Clinical trial design
• Therapeutics

Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 print issues and online access

195,33 € per year

only 16,28 € per issue

Buy this article

• Purchase on Springer Link
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Causal inference and counterfactual prediction in machine learning for actionable healthcare

Automated causal inference in application to randomized controlled clinical trials

Prediction of treatment outcome in clinical trials under a personalized medicine perspective

Kaddour, J., Lynch, A., Liu, Q., Kusner, M. J. & Silva, R. Causal machine learning: a survey and open problems. Preprint at arXiv https://doi.org/10.48550/arXiv.2206.15475 (2022).

Yoon, J., Jordon, J. & van der Schaar, M. GANITE: estimation of individualized treatment effects using generative adversarial nets. In Proc. 6th International Conference on Learning Representations (ICLR, 2018).

Evans, W. E. & Relling, M. V. Pharmacogenomics: translating functional genomics into rational therapeutics. Science 286 , 487–491 (1999).

Article   CAS   PubMed   Google Scholar  

Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25 , 24–29 (2019).

Kopitar, L., Kocbek, P., Cilar, L., Sheikh, A. & Stiglic, G. Early detection of type 2 diabetes mellitus using machine learning-based prediction models. Sci. Rep. 10 , 11981 (2020).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Alaa, A. M., Bolton, T., Di Angelantonio, E., Rudd, J. H. & van der Schaar, M. Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423,604 UK Biobank participants. PLoS ONE 14 , e0213653 (2019).

Cahn, A. et al. Prediction of progression from pre-diabetes to diabetes: development and validation of a machine learning model. Diabetes/Metab. Res. Rev. 36 , e3252 (2020).

Article   PubMed   Google Scholar  

Zueger, T. et al. Machine learning for predicting the risk of transition from prediabetes to diabetes. Diabetes Technol. Ther. 24 , 842–847 (2022).

Krittanawong, C. et al. Machine learning prediction in cardiovascular diseases: a metaanalysis. Sci. Rep. 10 , 16057 (2020).

Xie, Y. et al. Comparative effectiveness of SGLT2 inhibitors, GLP-1 receptor agonists, DPP-4 inhibitors, and sulfonylureas on risk of major adverse cardiovascular events: Emulation of a randomised target trial using electronic health records. Lancet Diabetes Endocrinol. 11 , 644–656 (2023).

Deng, Y. et al. Comparative effectiveness of second line glucose lowering drug treatments using real world data: emulation of a target trial. BMJ Med. 2 , e000419 (2023).

Article   PubMed   PubMed Central   Google Scholar  

Kalia, S. et al. Emulating a target trial using primary-care electronic health records: sodium glucose cotransporter 2 inhibitor medications and hemoglobin A1c. Am. J. Epidemiol. 192 , 782–789 (2023).

Petito, L. C. et al. Estimates of overall survival in patients with cancer receiving different treatment regimens: emulating hypothetical target trials in the Surveillance, Epidemiology, and End Results (SEER)–Medicare linked database. JAMA Netw. Open 3 , e200452 (2020).

Rubin, D. B. Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66 , 688–701 (1974).

Article   Google Scholar  

Rubin, D. B. Causal inference using potential outcomes: design, modeling, decisions. J. Am. Stat. Assoc. 100 , 322–331 (2005).

Article   CAS   Google Scholar  

Robins, J. M. Correcting for non-compliance in randomized trials using structural nested mean models. Commun. Stat. 23 , 2379–2412 (1994).

Robins, J. M. Robust estimation in sequentially ignorable missing data and causal inference models. In 1999 Proceedings of the American Statistical Association on Bayesian Statistical Science 6–10 (2000).

Holland, P. W. Statistics and causal inference. J. Am. Stat. Assoc. 81 , 945–960 (1986).

Pearl, J. Causality: Models , Reasoning , and Inference (Cambridge University Press, 2009).

Hemkens, L. G. et al. Interpretation of epidemiologic studies very often lacked adequate consideration of confounding. J. Clin. Epidemiol. 93 , 94–102 (2018).

Dang, L. E. et al. A causal roadmap for generating high-quality real-world evidence. J. Clin. Transl. Sci. 7 , e212 (2023).

Petersen, M. L. & van der Laan, M. J. Causal models and learning from data: integrating causal modeling and statistical estimation. Epidemiology 25 , 418–426 (2014).

van der Laan, M. J. & Rubin, D. Targeted maximum likelihood learning. Int. J. Biostatistics 2 , 11 (2006).

Hirano, K. & Imbens, G. W. in Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin’s Statistical Family (eds Gelman, A. & Meng, X.-L.) Ch. 7 (John Wiley & Sons, 2004).

Specht, L. et al. Modern radiation therapy for Hodgkin lymphoma: field and dose guidelines from the international lymphoma radiation oncology group (ILROG). Int. J. Radiat. Oncol. Biol. Phys. 89 , 854–862 (2014).

van Geloven, N. et al. Prediction meets causal inference: the role of treatment in clinical prediction models. Eur. J. Epidemiol. 35 , 619–630 (2020).

Kennedy, E. H. Towards optimal doubly robust estimation of heterogeneous causal effects. Electron. J. Stat. 17 , 3008–3049 (2023).

Imbens, G. W. & Rubin, D. B. Causal Inference in Statistics, Social, and Biomedical Sciences (Cambridge University Press, 2015).

Book   Google Scholar  

Chen, J., Vargas-Bustamante, A., Mortensen, K. & Ortega, A. N. Racial and ethnic disparities in health care access and utilization under the Affordable Care Act. Med. Care 54 , 140–146 (2016).

Cinelli, C., Forney, A. & Pearl, J. A crash course in good and bad controls. Sociol. Methods Res. https://doi.org/10.1177/00491241221099552 (2022).

Laffers, L. & Mellace, G. Identification of the average treatment effect when SUTVA is violated. Department of Economics SDU. Discussion Papers on Business and Economics No. 3 (University of Southern Denmark, 2020).

Huber, M. & Steinmayr, A. A framework for separating individual-level treatment effects from spillover effects. J. Bus. Econ. Stat. 39 , 422–436 (2021).

Syrgkanis, V. et al. Machine learning estimation of heterogeneous treatment effects with instruments. In Proc. 33rd International Conference on Neural Information Processing Systems (eds Wallach, H. M. & Larochelle, H.) 15193–15202 (NeurIPS, 2019).

Frauen, D. & Feuerriegel, S. Estimating individual treatment effects under unobserved confounding using binary instruments. In Proc. 11th International Conference on Learning Representations (ICLR, 2023).

Lim, B. Forecasting treatment responses over time using recurrent marginal structural networks. In Proc. Advances in Neural Information Processing Systems 31 (eds Bengio, H. et al.) (NeurIPS, 2018).

Liu, R., Yin, C. & Zhang, P. Estimating individual treatment effects with time-varying confounders. In Proc. IEEE International Conference on Data Mining (ICDM) 382–391 (IEEE, 2020).

Li, R. et al. G-Net: a deep learning approach to G-computation for counterfactual outcome prediction under dynamic treatment regimes. In Proc. Machine Learning for Health (eds Roy, S. et al.) 282–299 (PMLR, 2021).

Bica, I., Alaa, A. M., Jordon, J. & van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations. In Proc. 8th International Conference on Learning Representations 11790–11817 (ICLR, 2020).

Liu, R., Hunold, K. M., Caterino, J. M. & Zhang, P. Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis. Nat. Mach. Intell. 5 , 421–431 (2023).

Melnychuk, V., Frauen, D. & Feuerriegel, S. Causal transformer for estimating counterfactual outcomes. In Proc. 39th International Conference on Machine Learning (eds Chaudhuri, K. et al.) 15293–15329 (PMLR, 2022).

Schulam, P. & Saria, S. Reliable decision support using counterfactual models. In Proc. 31st International Conference on Neural Information Processing Systems (eds von Luxburg, U. et al.) 1696–1706 (NeurIPS, 2017).

Vanderschueren, T., Curth, A., Verbeke, W. & van der Schaar, M. Accounting for informative sampling when learning to forecast treatment outcomes over time. In Proc. 40th International Conference on Machine Learning (eds Krause, A. et al.) 34855–34874 (PMLR, 2023).

Seedat, N., Imrie, F., Bellot, A., Qian, Z. & van der Schaar, M. Continuous-time modeling of counterfactual outcomes using neural controlled differential equations. In Proc. 39th International Conference on Machine Learning (eds Chaudhuri, K. et al.) 19497–19521 (PMLR, 2022).

Hess, K., Melnychuk, V., Frauen, D. & Feuerriegel, S. Bayesian neural controlled differential equations for treatment effect estimation. In Proc. 12th International Conference on Learning Representations (ICLR, 2024).

Hatt, T., Berrevoets, J., Curth, A., Feuerriegel, S. & van der Schaar, M. Combining observational and randomized data for estimating heterogeneous treatment effects. Preprint at arXiv https://doi.org/10.48550/arXiv.2202.12891 (2022).

Colnet, B. et al. Causal inference methods for combining randomized trials and observational studies: a review. Stat. Sci. 39 , 165–191 (2024).

Kallus, N., Puli, A. M. & Shalit, U. Removing hidden confounding by experimental grounding. In Proc. 32nd Conference on Neural Information Processing Systems (eds Bengio, S. et al.) 10888–10897 (NeurIPS, 2018).

van der Laan, M. J., Polley, E. C. & Hubbard, A. E. Super learner. Stat. Appl. Genet. Mol. Biol. 6 , 25 (2007).

Google Scholar  

van der Laan, M. J. & Rose, S. Targeted Learning: Causal Inference for Observational and Experimental Data 1st edn (Springer, 2011).

Zheng, W. & van der Laan, M. J. in Targeted Learning: Causal Inference for Observational and Experimental Data 1st edn, 459–474 (Springer, 2011).

Díaz, I. & van der Laan, M. J. Targeted data adaptive estimation of the causal dose–response curve. J. Causal Inference 1 , 171–192 (2013).

Luedtke, A. R. & van der Laan, M. J. Super-learning of an optimal dynamic treatment rule. Int. J. Biostatistics 12 , 305–332 (2016).

Künzel, S. R., Sekhon, J. S., Bickel, P. J. & Yu, B. Metalearners for estimating heterogeneous treatment effects using machine learning. Proc. Natl Acad. Sci. USA 116 , 4156–4165 (2019).

Curth, A. & van der Schaar, M. Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms. In Proc. 24th International Conference on Artificial Intelligence and Statistics (eds Banerjee, A. & Fukumizu, K.) 1810–1818 (PMLR, 2021).

Athey, S. & Imbens, G. Recursive partitioning for heterogeneous causal effects. Proc. Natl Acad. Sci. USA 113 , 7353–7360 (2016).

Wager, S. & Athey, S. Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113 , 1228–1242 (2018).

Athey, S., Tibshirani, J. & Wager, S. Generalized random forests. Ann. Stat. 47 , 1148–1178 (2019).

Shalit, U., Johansson, F. D. & Sontag, D. Estimating individual treatment effect: generalization bounds and algorithms. In Proc. 34th International Conference on Machine Learning (eds Precup, D. & Teh, Y. W.) 3076–3085 (PMLR, 2017).

Shi, C., Blei, D. & Veitch, V. Adapting neural networks for the estimation of treatment effects. In Proc. 33rd International Conference on Neural Information Processing Systems (eds Wallach, H. M. et al.) 2496–2506 (NeurIPS, 2019).

Bach, P., Chernozhukov, V., Kurz, M. S. & Spindler, M. DoubleML: an object-oriented implementation of double machine learning in Python. J. Mach. Learn. Res. 23 , 2469–2474 (2022).

Foster, D. J. & Syrgkanis, V. Orthogonal statistical learning. Ann. Stat. 51 , 879–908 (2023).

Kennedy, E. H., Ma, Z., McHugh, M. D. & Small, D. S. Nonparametric methods for doubly robust estimation of continuous treatment effects. J. R. Stat. Soc. Series B Stat. Methodol. 79 , 1229–1245 (2017).

Nie, L., Ye, M., Liu, Q. & Nicolae, D. VCNet and functional targeted regularization for learning causal effects of continuous treatments. In Proc. 9th International Conference on Learning Representations (ICLR, 2021).

Bica, I., Jordon, J. & van der Schaar, M. Estimating the effects of continuous-valued interventions using generative adversarial networks. In Proc. 34th Annual Conference on Neural Information Processing Systems (eds Larochelle, H. et al.) (NeurIPS, 2020).

Hill, J. L. Bayesian nonparametric modeling for causal inference. J. Computational Graph. Stat. 20 , 217–240 (2011).

Schwab, P., Linhardt, L., Bauer, S., Buhmann, J. M. & Karlen, W. Learning counterfactual representations for estimating individual dose-response curves. In Proc. 34th AAAI Conference on Artificial Intelligence 5612–5619 (AAAI, 2020).

Schweisthal, J., Frauen, D., Melnychuk, V. & Feuerriegel, S. Reliable off-policy learning for dosage combinations. In Proc. 37th Annual Conference on Neural Information Processing Systems (NeurIPS, 2023).

Melnychuk, V., Frauen, D. & Feuerriegel, S. Normalizing flows for interventional density estimation. In Proc. 40th International Conference on Machine Learning (eds Krause, A. et al.) 24361–24397 (PMLR, 2023).

Banerji, C. R., Chakraborti, T., Harbron, C. & MacArthur, B. D. Clinical AI tools must convey predictive uncertainty for each individual patient. Nat. Med. 29 , 2996–2998 (2023).

Alaa, A. M. & van der Schaar, M. Bayesian inference of individualized treatment effects using multi-task Gaussian processes. In Proc. 31st Annual Conference on Neural Information Processing Systems (eds von Luxburg, U. et al.) 3425–3433 (NeurIPS, 2017).

Alaa, A., Ahmad, Z. & van der Laan, M. Conformal meta-learners for predictive inference of individual treatment effects. In Proc. 37th Annual Conference on Neural Information Processing Systems (eds Oh, A. et al.) (NeurIPS, 2023).

Curth, A., Svensson, D., Weatherall, J. & van der Schaar, M. Really doing great at estimating CATE? A critical look at ML benchmarking practices in treatment effect estimation. In Proc. 35th Conference on Neural Information Processing Systems Datasets and Benchmarks Track (eds Vanschoren, J. & Yeung, S.-K.) (NeurIPS, 2021).

Boyer, C. B., Dahabreh, I. J. & Steingrimsson, J. A. Assessing model performance for counterfactual predictions. Preprint at arXiv https://doi.org/10.48550/arXiv.2308.13026 (2023).

Keogh, R. H. & van Geloven, N. Prediction under interventions: evaluation of counterfactual performance using longitudinal observational data. Preprint at arXiv https://doi.org/10.48550/arXiv.2304.10005 (2023).

Curth, A. & van der Schaar, M. In search of insights, not magic bullets: towards demystification of the model selection dilemma in heterogeneous treatment effect estimation. In Proc. 40th International Conference on Machine Learning (eds Krause, A. et al.) 6623–6642 (PMLR, 2023).

Sharma, A., Syrgkanis, V., Zhang, C. & Kıcıman, E. DoWhy: addressing challenges in expressing and validating causal assumptions. Preprint at arXiv https://doi.org/10.48550/arXiv.2108.13518 (2021).

Vokinger, K. N., Feuerriegel, S. & Kesselheim, A. S. Mitigating bias in machine learning for medicine. Commun. Med. 1 , 25 (2021).

Petersen, M. L., Porter, K. E., Gruber, S., Wang, Y. & van der Laan, M. J. Diagnosing and responding to violations in the positivity assumption. Stat. Methods Med. Res. 21 , 31–54 (2012).

Jesson, A., Mindermann, S., Shalit, U. & Gal, Y. Identifying causal-effect inference failure with uncertainty-aware models. In Proc. 34th Conference on Neural Information Processing Systems (eds Larochelle, H. et al.) 11637–11649 (NeurIPS, 2020).

Rudolph, K. E. et al. When effects cannot be estimated: redefining estimands to understand the effects of naloxone access laws. Epidemiology 33 , 689–698 (2022).

Cornfield, J. et al. Smoking and lung cancer: recent evidence and a discussion of some questions. J. Natl Cancer Inst. 22 , 173–203 (1959).

CAS   PubMed   Google Scholar  

Frauen, D., Melnychuk, V. & Feuerriegel, S. Sharp bounds for generalized causal sensitivity analysis. In Proc. 37th Annual Conference on Neural Information Processing Systems (eds Oh, A. et al.) (NeurIPS, 2023).

Kallus, N., Mao, X. & Zhou, A. Interval estimation of individual-level causal effects under unobserved confounding. In Proc. 22nd International Conference on Artificial Intelligence and Statistics (eds Chaudhuri, K. & Sugiyama, M.) 2281–2290 (PMLR, 2019).

Jin, Y., Ren, Z. & Candès, E. J. Sensitivity analysis of individual treatment effects: a robust conformal inference approach. Proc. Natl Acad. Sci. USA 120 , e2214889120 (2023).

Dorn, J. & Guo, K. Sharp sensitivity analysis for inverse propensity weighting via quantile balancing. J. Am. Stat. Assoc. 118 , 2645–2657 (2023).

Oprescu, M. et al. B-learner: quasi-oracle bounds on heterogeneous causal effects under hidden confounding. In Proc. 40th International Conference on Machine Learning (eds Krause, A. et al.) 26599–26618 (PMLR, 2023).

Hernán, M. A. & Robins, J. M. Using big data to emulate a target trial when a randomized trial is not available. Am. J. Epidemiol. 183 , 758–764 (2016).

Xu, J. et al. Protocol for the development of a reporting guideline for causal and counterfactual prediction models in biomedicine. BMJ Open 12 , e059715 (2022).

Fournier, J. C. et al. Antidepressant drug effects and depression severity: a patient-level meta-analysis. JAMA 303 , 47–53 (2010).

Booth, C. M., Karim, S. & Mackillop, W. J. Real-world data: towards achieving the achievable in cancer care. Nat. Rev. Clin. Oncol. 16 , 312–325 (2019).

Chien, I. et al. Multi-disciplinary fairness considerations in machine learning for clinical trials. In Proc. 2022 ACM Conference on Fairness , Accountability , and Transparency (FACCT '22) 906–924 (ACM, 2022).

Ross, E. L. et al. Estimated average treatment effect of psychiatric hospitalization in patients with suicidal behaviors: a precision treatment analysis. JAMA Psychiatry 81 , 135–143 (2023).

Cole, S. R. & Stuart, E. A. Generalizing evidence from randomized clinical trials to target populations: the ACTG 320 trial. Am. J. Epidemiol. 172 , 107–115 (2010).

Hatt, T., Tschernutter, D. & Feuerriegel, S. Generalizing off-policy learning under sample selection bias. In Proc. 38th Conference on Uncertainty in Artificial Intelligence (eds Cussens, J. & Zhang, K.) 769–779 (PMLR, 2022).

Sherman, R. E. et al. Real-world evidence—what is it and what can it tell us. N. Engl. J. Med. 375 , 2293–2297 (2016).

Norgeot, B. et al. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat. Med. 26 , 1320–1324 (2020).

Von Elm, E. et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies. Lancet 370 , 1453–1457 (2007).

Nie, X. & Wager, S. Quasi-oracle estimation of heterogeneous treatment effects. Biometrika 108 , 299–319 (2021).

Chernozhukov, V. et al. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21 , C1–C68 (2018).

Morzywołek, P., Decruyenaere, J. & Vansteelandt, S. On a general class of orthogonal learners for the estimation of heterogeneous treatment effects. Preprint at arXiv https://doi.org/10.48550/arXiv.2303.12687 (2023).

Download references

Acknowledgements

S.F. acknowledges funding via Swiss National Science Foundation Grant 186932.

Author information

Authors and affiliations.

LMU Munich, Munich, Germany

Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal & Konstantin Hess

Munich Center for Machine Learning, Munich, Germany

Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal, Konstantin Hess & Niki Kilbertus

Department of Applied Mathematics & Theoretical Physics, University of Cambridge, Cambridge, UK

Alicia Curth

School of Computation, Information and Technology, TU Munich, Munich, Germany

Stefan Bauer & Niki Kilbertus

Helmholtz Munich, Munich, Germany

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

Isaac S. Kohane

Cambridge Centre for AI in Medicine, University of Cambridge, Cambridge, UK

Mihaela van der Schaar

The Alan Turing Institute, London, UK

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed to conceptualization, manuscript writing and approval of the manuscript.

Corresponding author

Correspondence to Stefan Feuerriegel .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Medicine thanks Matthew Sperrin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Karen O’Leary, in collaboration with the Nature Medicine team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Feuerriegel, S., Frauen, D., Melnychuk, V. et al. Causal machine learning for predicting treatment outcomes. Nat Med 30 , 958–968 (2024). https://doi.org/10.1038/s41591-024-02902-1

Download citation

Received : 03 January 2024

Accepted : 04 March 2024

Published : 19 April 2024

Issue Date : April 2024

DOI : https://doi.org/10.1038/s41591-024-02902-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• Contributors
• Sign Up Here!

Diagnostic frameworks

• Illness scripts
• Residency Match
• Publications

All Frameworks

Abdominal Distension

Abdominal Pain Overview

Abdominal Pain – Image Negative

Abdominal Pain – Image Negative – Action Steps

Abdominal Pain Thought Train

Acute Mesenteric Ischemia

Acute Pancreatitis

AKI – overview

AKI and cancer

Aldosterone – Inappropriate

Altered mental status 1.0

Altered Mental Status 2.0

Altered Mental Status – MIST Negative

Anemia Thought Train

Antibiotic Failure

Aortitis: Paths to the Problem

Bilateral Lower Extremity Weakness

Bone lesions

Bony Back Pain – Overview

Brain Abscess

Bronchiectasis

Bowel Obstruction

Bowel Perforation

Bowel Wall Thickening

Cancer and Hypercalcemia

Cervical Lymphadenopathy

CHF – Hyperacute Heart Failure

Chronic Cough

Chronic Diarrhea Overview

Chronic Diarrhea: 4 quarters approach

Chronic Kidney Disease

Chylothorax

Cirrhosis and Dyspnea

Coagulopathy – Elevated INR and PTT

Community Acquired Pneumonia

Community Acquired Pneumonia – Ddx

Congestive Heart Failure – HFrEF

Constipation

CSF Lymphocytic Pleocytosis

Diabetes – Causes

Diabetes Insipidus

Diarrhea Thought Train

Diffuse Alveolar Hemorrhage – DAH

Diffuse Lymphadenopathy Overview

Diffuse Lymphadenopathy: A Guide 

Diverticulitis Complications

Doxycycline Deficiency State

Dyspnea and Cancer

Dyspnea Pyramid

Eosinophilia

Extra-Hepatic Biliary Obstruction

Fever Overview

Fever and Back Pain

Fever and rash

Fever in a returning traveler

Fever – Path to Inflammation

Focal Splenic Lesion

Gastric Mass

Glomerulonephritis

Gram Positive Organisms

Granulomatous Infection – by exposure

HCV – Extrahepatic Manifestations

HCV – Skin Manifestations

Headache (Secondary)

Headache (Thunderclap)

Heart Failure and Weight Loss

Hemolysis: Autoimmune Hemolytic Anemia (AIHA)

Hemolysis: Chronic Hemolysis Complications

Hemolysis: Hemolytic Anemia

Hemolysis: Microangiopathic Hemolytic Anemia (MAHA)

Hemoptysis: The Arc

Hemoptysis: Overview

Hemoptysis: Massive

HIV overview

HIV & Chronic Diarrhea

HIV & Infection

HIV: Non-infectious complications

Hyperammonemia

Hypercalcemia

Hyperkalemia

Hypernatremia

Hypertension Overview

Hypertension – Secondary

Hypoalbuminemia

Hypocalcemia

Hypoglycemia

Hypokalemia – Overview

Hypokalemia – Practical Approach

Hyponatremia

Hypophosphatemia- Overview

Hypophosphatemia – Severe

Hypothermia

Hypothyroidism

Hypoxemia Thought Train

IgM Disorders

Indirect Hyperbilirubinemia

Infection in the inpatient 1.0

Infection in the Inpatient – Secondary Evaluation

Infectious Aortitis: Clues

Inflammation overview

Inflammation Thought Train

Inflammatory Abdominal Pain

Inguinal Lymphadenopathy

Intrarenal AKI

Iron deficiency anemia

Jaundice – Overview

Jaundice – Intrahepatic Cholestasis

Jaundice Thought Train

Kidney Stone Overview

Lactic Acidosis

Lactate Dehydrogenase – LDH

Low Back Pain – Overview

Lung Cancer

Lung cavity

Lung Nodules

Mediastinal Mass

Macrocytic Anemia

Metabolic Acidosis Overview

Metabolic Acidosis: AGMA

Metabolic Acidosis: NAGMA

Metabolic Alkalosis

Monoarticular Arthritis

Nausea and Vomiting Thought Train

Neurologic Complications of Systemic Cancer

Nonstress Fracture

Optic Disc Swelling

Pancreatic Mass

Pancreatic Mass: Simple Approach

Pancytopenia

Parkinson’s Dz – The Time Course

Parkinsonism

Peptic Ulcer Disease

Peripheral Edema

Peripheral Neuropathy

Peritonitis

Petechial Rash

Pharyngitis

Photophobia

Pituitary Adenoma

Pleural effusion

Pleuritic Chest Pain

Pleural Nodularity

Pneumonia Complications

Pneumothorax

Polycythemia

Portal Vein Thrombosis

Postprandial Abdominal Pain

Post-renal AKI

Primary Respiratory Alkalosis

Pulmonary disease with eosinophilia

Pulmonary Function Tests – PFTs

Pulmonary Hypertension

Pulmonary Renal Syndromes

Raynaud’s Syndrome

Severe Acute Liver Injury

Splenic Infarct

Splenic Infarct vs Abscess

Splenomegaly

Specific Back Pain Overview  

Subacute Dyspnea on Exertion

Sudden Symptoms

Supraventricular tachycardia – SVT

Thorax and Inflammation

Thrombocytopenia

Thrombocytosis

Thrombosis- Overview

Thrombosis – Bland

Thrombosis on Anticoagulation

Thrombosis – Ultrahypercoagulable

Thyroid Function Test in the Evaluation of Hyperthyroidism

Thyrotoxicosis

Tick borne infection

Transverse Myelitis

Upper GI Bleed

Urinalysis interpretation

Urinary Retention

Urinary Tract Infection

Vaginal Bleeding

Vasculitis Mimics

Vasculopathy – Systemic

Vasculopathy – Skin

Vasculopathy – CNS

Visceral Air

Visual Field Defects

Weakness Thought Train

Weight loss

Wide complex tachycardia