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Activity Number: 216 - Promises and Pitfalls of Making Decisions with Real World Data
Type: Invited
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #300271
Title: Learning Treatment Strategies from Randomized Trials Supplemented by Information in Electronic Health Records
Author(s): Yuanjia Wang*
Companies: Columbia University
Keywords: Precision medicine; Machine learning; Observational studies; Real world evidence

Individualized treatment rules (ITRs) tailor medical treatments according to patient-specific features in order to improve treatment outcomes. Recently, methods for estimating ITRs from randomized controlled trials (RCTs) and observational studies (e.g. electronic health records, EHRs) have increasingly received attention. Since EHRs document treatment prescription in the real world, transferring information learned from EHRs to RCTs, if done appropriately, could improve the performance of ITRs, especially in the general population. The strategy of transferring information is referred as domain adaptation. In this work, we propose a domain adaptation machine learning method to enhance learning ITRs from RCT supplemented by information from EHRs. Specifically, we achieve domain adaptation through supervised learning feature extraction and projection. We apply our framework to transfer information learned from EHRs of type 2 diabetes (T2D) patients to improve optimizing insulin therapies learned from a RCT.

Authors who are presenting talks have a * after their name.

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