Learning Optimal Individualized Treatment Rules from Electronic Health Record Data.

TitleLearning Optimal Individualized Treatment Rules from Electronic Health Record Data.
Publication TypeJournal Article
Year of Publication2016
AuthorsWang, Yuanjia, Peng Wu, Ying Liu, Chunhua Weng, and Donglin Zeng
JournalIEEE Int Conf Healthc Inform
Volume2016
Pagination65-71
Date Published2016 Oct
ISSN2575-2626
Abstract

Medical research is experiencing a paradigm shift from "one-size-fits-all" strategy to a precision medicine approach where the right therapy, for the right patient, and at the right time, will be prescribed. We propose a statistical method to estimate the optimal individualized treatment rules (ITRs) that are tailored according to subject-specific features using electronic health records (EHR) data. Our approach merges statistical modeling and medical domain knowledge with machine learning algorithms to assist personalized medical decision making using EHR. We transform the estimation of optimal ITR into a classification problem and account for the non-experimental features of the EHR data and confounding by clinical indication. We create a broad range of feature variables that reflect both patient health status and healthcare data collection process. Using EHR data collected at Columbia University clinical data warehouse, we construct a decision tree for choosing the best second line therapy for treating type 2 diabetes patients.

DOI10.1109/ICHI.2016.13
Alternate JournalIEEE Int Conf Healthc Inform
Original PublicationLearning optimal individualized treatment rules from electronic health record data.
PubMed ID28503676
PubMed Central IDPMC5423731
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 NS073671 / NS / NINDS NIH HHS / United States
U01 NS082062 / NS / NINDS NIH HHS / United States