Matched Learning for Optimizing Individualized Treatment Strategies Using Electronic Health Records.

ABSTRACT: Current guidelines for treatment decision making largely rely on data from randomized controlled trials (RCTs) studying average treatment effects. They may be inadequate to make individualized treatment decisions in real-world settings. Large-scale electronic health records (EHR) provide opportunities to fulfill the goals of personalized medicine and learn individualized treatment rules (ITRs) depending on patient-specific characteristics from real-world patient data. In this work, we tackle challenges with EHRs and propose a machine learning approach based on matching (M-learning) to estimate optimal ITRs from EHRs. This new learning method performs matching instead of inverse probability weighting as commonly used in many existing methods for estimating ITRs to more accurately assess ind

SUBMITTER: Wu P 

PROVIDER: S-EPMC7539620 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature