ArticleJournal of the American Medical Informatics Association : JAMIA2019
Applying machine learning to predict real-world individual treatment effects: insights from a virtual patient cohort.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- An interpretable survival benefit analytics framework for optimizing cancer treatment decision-making.Medical & biological engineering & computing · 2026Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- A Transformer-Based Framework for Counterfactual Estimation of Antihypertensive Treatment Effect on COVID-19 Infection Risk - A Proof-of-Concept Study.American journal of hypertension · 2025Article
- How to select predictive models for decision-making or causal inference.GigaScience · 2025Article
- The Use of Artificial Intelligence to Predict the Prognosis of Patients Undergoing Central Nervous System Rehabilitation: A Narrative Review.Healthcare (Basel, Switzerland) · 2023Review
- Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose.Artificial intelligence in medicine · 2022Article
- Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study.BMC medical informatics and decision making · 2022Article
- Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus.Methods of information in medicine · 2021Article
- Causal AI with Real World Data: Do Statins Protect from Alzheimer's Disease Onset?ICMHI 2021 : 2021 5th International Conference on Medical and Health Informatics : May 14-16, 2021, Kyoto, Japan. International Conference on Medical and Health Informatics (5th : 2021 : Online) · 2021Article
- Evaluating sources of bias in observational studies of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker use during COVID-19: beyond confounding.Journal of hypertension · 2021Review
- A theoretical model of health management using data-driven decision-making: the future of precision medicine and health.Journal of translational medicine · 2021Article
- Article
- Clinical Research Informatics.Yearbook of medical informatics · 2020Review
- Artificial intelligence approaches using natural language processing to advance EHR-based clinical research.The Journal of allergy and clinical immunology · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveWe aimed to investigate bias in applying machine learning to predict real-world individual treatment effects. MATERIALS AND
methodsUsing a virtual patient cohort, we simulated real-world healthcare data and applied random forest and gradient boosting classifiers to develop prediction models. Treatment effect was estimated as the difference between the predicted outcomes of a treatment and a control. We evaluated the impact of predictors (ie, treatment predictors [X1], confounders [X2], treatment effects modifiers [X3], and other outcome risk factors [X4]) with known effects on treatment and outcome using real-world data, and outcome imbalance on predicting individual outcome. Using counterfactuals, we evaluated percentage of patients with biased predicted individual treatment effects.
resultsThe X4 had relatively more impact on model performance than X2 and X3 did. No effects were observed from X1. Moderate-to-severe outcome imbalance had a significantly negative impact on model performance, particularly among subgroups in which an outcome occurred. Bias in predicting individual treatment effects was significant and persisted even when the models had a 100% accuracy in predicting health outcome. DISCUSSION: Inadequate inclusion of the X2, X3, and X4 and moderate-to-severe outcome imbalance may affect model performance in predicting individual outcome and subsequently bias in predicting individual treatment effects. Machine learning models with all features and high performance for predicting individual outcome still yielded biased individual treatment effects.
conclusionsDirect application of machine learning might not adequately address bias in predicting individual treatment effects. Further method development is needed to advance machine learning to support individualized treatment selection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.