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Applying machine learning to predict real-world individual treatment effects: insights from a virtual patient cohort

https://doi.org/10.1093/jamia/ocz036
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Machine Learning and the Profession of Medicine
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The Precision Medicine Initiative
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Big Data and Machine Learning in Health Care
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Limitations of Applying Summary Results of Clinical Trials to Individual Patients
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Development and Application of a Machine Learning Approach to Assess Short-term Mortality Risk Among Patients With Cancer Starting Chemotherapy
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Development and Validation of an Electronic Health Record–Based Machine Learning Model to Estimate Delirium Risk in Newly Hospitalized Patients Without Known Cognitive Impairment
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Challenges in Applying the Results of Clinical Trials to Clinical Practice
inferredcites →https://doi.org/10.1002/phar.2051openalex-snapshot
Incidence of and Risk Factors for Severe Adverse Events in Elderly Patients Taking Angiotensin‐Converting Enzyme Inhibitors or Angiotensin II Receptor Blockers after an Acute Myocardial Infarction
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Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
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Applied Predictive Modeling
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Applying Resampling Methods for Imbalanced Datasets to Not So Imbalanced Datasets
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Extremely randomized trees
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Interpreting treatment-effect estimates with heterogeneity and choice: Simulation model results
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Adherence Tradeoff to Multiple Preventive Therapies and All-Cause Mortality After Acute Myocardial Infarction
inferredcites →https://doi.org/10.1016/j.jval.2014.12.005openalex-snapshot
Potential Application of Machine Learning in Health Outcomes Research and Some Statistical Cautions
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Cost-sensitive boosting for classification of imbalanced data
inferredcites →https://doi.org/10.1016/s0140-6736(95)90120-5openalex-snapshot
Can overall results of clinical trials be applied to all patients?
inferredcites →https://doi.org/10.1021/ci025626iopenalex-snapshot
Assessing Model Fit by Cross-Validation
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1037/h0037350openalex-snapshot
Estimating causal effects of treatments in randomized and nonrandomized studies.
inferredcites →https://doi.org/10.1056/nejmp1500523openalex-snapshot
A New Initiative on Precision Medicine
inferredcites →https://doi.org/10.1056/nejmp1606181openalex-snapshot
Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
inferredcites →https://doi.org/10.1093/aje/kwr283openalex-snapshot
Apples and Oranges? Interpretations of Risk Adjustment and Instrumental Variable Estimates of Intended Treatment Effects Using Observational Data
inferredcites →https://doi.org/10.1093/bioinformatics/bti499openalex-snapshot
Prediction error estimation: a comparison of resampling methods
inferredcites →https://doi.org/10.1093/biomet/70.1.41openalex-snapshot
The central role of the propensity score in observational studies for causal effects
inferredcites →https://doi.org/10.1109/tkde.2002.1000348openalex-snapshot
An instance-weighting method to induce cost-sensitive trees
inferredcites →https://doi.org/10.1111/j.0081-1750.2006.00164.xopenalex-snapshot
1. The Scientific Model of Causality
inferredcites →https://doi.org/10.1111/j.0887-378x.2004.00327.xopenalex-snapshot
Evidence‐Based Medicine, Heterogeneity of Treatment Effects, and the Trouble with Averages
inferredcites →https://doi.org/10.1126/science.3287615openalex-snapshot
Measuring the Accuracy of Diagnostic Systems
inferredcites →https://doi.org/10.1136/bmj.i6openalex-snapshot
Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests
inferredcites →https://doi.org/10.1142/s0218001409007326openalex-snapshot
CLASSIFICATION OF IMBALANCED DATA: A REVIEW
inferredcites →https://doi.org/10.1145/2939672.2939785openalex-snapshot
XGBoost
inferredcites →https://doi.org/10.1161/circulationaha.115.001593openalex-snapshot
Machine Learning in Medicine
inferredcites →https://doi.org/10.1161/jaha.115.001798openalex-snapshot
Effectiveness and Safety of Dabigatran and Warfarin in Real‐World US Patients With Non‐Valvular Atrial Fibrillation: A Retrospective Cohort Study
inferredcites →https://doi.org/10.1161/jaha.117.007106openalex-snapshot
Changes in Statin Adherence Following an Acute Myocardial Infarction Among Older Adults: Patient Predictors and the Association With Follow‐Up With Primary Care Providers and/or Cardiologists
inferredcites →https://doi.org/10.1201/9781315139470openalex-snapshot
Classification And Regression Trees
inferredcites →https://doi.org/10.1214/aos/1013203451openalex-snapshot
Greedy function approximation: A gradient boosting machine.
inferredcites →https://doi.org/10.1515/9781400883868openalex-snapshot
Mathematical Methods of Statistics (PMS-9)
inferredcites →https://doi.org/10.2307/2530946openalex-snapshot
Classification and Regression Trees.
inferredcites →https://doi.org/10.4230/lipics.esem.2026.21openalex-snapshot
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
inferredcites →https://doi.org/10.48550/arxiv.1603.02754openalex-snapshot
XGBoost: A Scalable Tree Boosting System
inferredcites →https://doi.org/10.7326/0003-4819-127-8_part_2-199710151-00064openalex-snapshot
Estimating Causal Effects from Large Data Sets Using Propensity Scores

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Leveraging visible-near-infrared spectroscopy and machine learning to detect nickel contamination in soil: Addressing class imbalances for environmental management
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Artificial intelligence approaches using natural language processing to advance EHR-based clinical research
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A scoping review of artificial intelligence applications in clinical trial risk assessment
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Clinical Research Informatics
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Factors Associated with Tobacco Cessation Services Request Among Users of an Online Self-Screening Questionnaire
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A Transformer-Based Framework for Counterfactual Estimation of Antihypertensive Treatment Effect on COVID-19 Infection Risk - A Proof-of-Concept Study
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How to select predictive models for decision-making or causal inference
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Scalable confounding adjustment in real-world evidence: benchmarking data-adaptive and investigator-specified strategies in a large-scale trial emulation study
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Evaluating sources of bias in observational studies of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker use during COVID-19: beyond confounding
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Hierarchical visual case-based reasoning for supporting breast cancer therapy
inferred← citeshttps://doi.org/10.1145/3394486.3403286openalex-snapshot
Predicting Individual Treatment Effects of Large-scale Team Competitions in a Ride-sharing Economy
inferred← citeshttps://doi.org/10.1145/3472813.3473206openalex-snapshot
Causal AI with Real World Data: Do Statins Protect from Alzheimer's Disease Onset?
inferred← citeshttps://doi.org/10.1146/annurev-publhealth-040119-094437openalex-snapshot
Machine Learning in Epidemiology and Health Outcomes Research
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Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants
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Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study
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A theoretical model of health management using data-driven decision-making: the future of precision medicine and health
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Predicting the individual effects of team competition on college students’ academic performance in mobile edge computing
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Predicting malaria epidemics in Burkina Faso with machine learning
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Problems of using artificial intelligence technologies in clinical drug research
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Machine Learning Analysis to Identify Factors Associated with Requesting Tobacco Cessation Services Among Users of an Online Self-Diagnostic Questionnaire in Mexico.
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Bioinformatics tools in protein analysis: Structure prediction, interaction modelling, and function relationship
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The Use of Artificial Intelligence to Predict the Prognosis of Patients Undergoing Central Nervous System Rehabilitation: A Narrative Review
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Additional file 1 of A theoretical model of health management using data-driven decision-making: the future of precision medicine and health
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