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Activity Number: 34 - Foundations in Bayesian Statistics
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #301862 Presentation
Title: Bayesian Double Feature Allocation for Phenotyping with Electronic Health Records
Author(s): Yang Ni* and Peter Müller and Yuan Ji
Companies: Texas A&M University and University of Texas Austin and The University of Chicago
Keywords: Indian buffet process; overlapping clustering; tripartite networks; unsupervised learning; matrix factorization

We propose a categorical matrix factorization method to infer latent diseases from electronic health records (EHR) data in an unsupervised manner. A latent disease is defined as an unknown biological aberration that causes a set of common symptoms for a group of patients. The proposed approach is based on a novel double feature allocation model which simultaneously allocates features to the rows and the columns of a categorical matrix. Using a Bayesian approach, available prior information on known diseases greatly improves identifiability and interpretability of latent diseases. This includes known diagnoses for patients and known association of diseases with symptoms. We validate the proposed approach by simulation studies including misspecified models and comparison with sparse latent factor models. In the application to Chinese EHR data, we find interesting results, some of which agree with related clinical and medical knowledge.

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

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