Applying machine learning to predict real-world individual treatment effects: insights from a virtual patient cohort. (10th April 2019)

• Record Type: Journal Article
• Authors: Fang, Gang; Annis, Izabela E; Elston-Lafata, Jennifer; Cykert, Samuel
• Is Part Of: Journal of the American Medical Informatics Association. Volume 26:Number 10(2019)
• Page Start: 977
• Page End: 988
• Publication Date: 2019-04-10
• Subjects: precision medicine -- machine learning -- comparative treatment effectiveness -- real-world evidence -- virtual patient cohort; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocz036
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: We aimed to investigate bias in applying machine learning to predict real-world individual treatment effects. Materials and Methods: Using 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 Results: The 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. Conclusions: Direct 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.