Clinical Phenotyping with an Outcomes-driven Mixture of Experts for Patient Matching and Risk Estimation

Author:

Hurley Nathan C.1ORCID,Dhruva Sanket S.2ORCID,Desai Nihar R.3ORCID,Ross Joseph R.3ORCID,Ngufor Che G.4ORCID,Masoudi Frederick5ORCID,Krumholz Harlan M.3ORCID,Mortazavi Bobak J.1ORCID

Affiliation:

1. Texas A&M University, USA

2. University of California San Francisco, USA

3. Yale University, USA

4. Mayo Clinic, USA

5. Ascension Health, USA

Abstract

Observational medical data present unique opportunities for analysis of medical outcomes and treatment decision making. However, because these datasets do not contain the strict pairing of randomized control trials, matching techniques are to draw comparisons among patients. A key limitation to such techniques is verification that the variables used to model treatment decision making are also relevant in identifying the risk of major adverse events. This article explores a deep mixture of experts approach to jointly learn how to match patients and model the risk of major adverse events in patients. Although trained with information regarding treatment and outcomes, after training, the proposed model is decomposable into a network that clusters patients into phenotypes from information available before treatment. This model is validated on a dataset of patients with acute myocardial infarction complicated by cardiogenic shock. The mixture of experts approach can predict the outcome of mortality with an area under the receiver operating characteristic curve of 0.85 ± 0.01 while jointly discovering five potential phenotypes of interest. The technique and interpretation allow for identifying clinically relevant phenotypes that may be used both for outcomes modeling as well as potentially evaluating individualized treatment effects.

Funder

Food and Drug Administration

U.S. Department of Health and Human Services

American College of Cardiology Foundation’s National Cardiovascular Data Registry

Publisher

Association for Computing Machinery (ACM)

Subject

Health Information Management,Health Informatics,Computer Science Applications,Biomedical Engineering,Information Systems,Medicine (miscellaneous),Software

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