Unsupervised Probabilistic Models for Sequential Electronic Health Records

TL;DR

This paper introduces an unsupervised probabilistic model for analyzing heterogeneous and sequential electronic health records, enabling subgroup identification and dynamic analysis of complex medical data.

Contribution

It presents a novel layered mixture model that captures underlying structure and dynamics in heterogeneous EHR data for the first time.

Findings

Model reveals meaningful patient subgroups.

Enables analysis of sequences related to mortality risk.

Provides new insights into complex EHR data.

Abstract

We develop an unsupervised probabilistic model for heterogeneous Electronic Health Record (EHR) data. Utilizing a mixture model formulation, our approach directly models sequences of arbitrary length, such as medications and laboratory results. This allows for subgrouping and incorporation of the dynamics underlying heterogeneous data types. The model consists of a layered set of latent variables that encode underlying structure in the data. These variables represent subject subgroups at the top layer, and unobserved states for sequences in the second layer. We train this model on episodic data from subjects receiving medical care in the Kaiser Permanente Northern California integrated healthcare delivery system. The resulting properties of the trained model generate novel insight from these complex and multifaceted data. In addition, we show how the model can be used to analyze sequences that contribute to assessment of mortality likelihood.