The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records

Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, George Hripcsak

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations

Abstract

The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Recently, large-scale electronic health records (EHRs) have become available, opening up new opportunities for more cost-effective assessments. However, assessing a treatment effect from EHRs is challenging: it is biased by unobserved confounders, unmeasured variables that affect both patients' medical prescription and their outcome, e.g. the patients' social economic status. To adjust for unobserved confounders, we develop the medical deconfounder, a machine learning algorithm that unbiasedly estimates treatment effects from EHRs. The medical deconfounder first constructs a substitute confounder by modeling which medications were prescribed to each patient; this substitute confounder is guaranteed to capture all multi-medication confounders, observed or unobserved (Wang and Blei, 2018). It then uses this substitute confounder to adjust for the confounding bias in the analysis. We validate the medical deconfounder on two simulated and two real medical data sets. Compared to classical approaches, the medical deconfounder produces closer-to-truth treatment effect estimates; it also identifies effective medications that are more consistent with the findings in the medical literature.

Original languageEnglish
Pages (from-to)490-512
Number of pages23
JournalProceedings of Machine Learning Research
Volume106
StatePublished - 2019
Event4th Machine Learning for Healthcare Conference, MLHC 2019 - Ann Arbor, United States
Duration: Aug 9 2019Aug 10 2019

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