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Activity Number: 31 - Personalized/Precision Medicine II
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #302970
Title: Domain Adaption Machine Learning for Optimizing Treatment Strategies in Randomized Trials by Leveraging Electronic Health Records
Author(s): Peng Wu* and Yuanjia Wang
Companies: Columbia University and Columbia University
Keywords: Personalized Medicine; Machine Learning; Real Word Evidence; Domain Adaption

Data from randomized controlled trials (RCTs) are used to infer valid individualized treatment rules (ITRs) using machine learning methods. However, RCTs are usually conducted under certain criteria, thus limiting the generalizability of ITRs to a broader population. Since patient's electronic health records (EHRs) document treatment prescriptions in the real world, transferring information in EHRs to RCTs could potentially improve the performance of ITRs. In this work, we propose a new domain adaptation method to learn ITRs by incorporating evidence from EHRs. We first pre-train "super" features from EHRs that summarize physicians' treatment decisions in the real world and then include super features to augment the feature space of the RCT and learn the optimal ITRs stratifying by these features. We adopt Q-learning and a modi fed matched-learning algorithm for estimation. We present justifications of our proposed method and conduct simulation studies to demonstrate its superiority. Finally, we apply our method to transfer information learned from EHRs of T2D patients to improve learning individualized insulin therapies from a RCT.

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

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