Identifying Patterns of Associated-Conditions through Topic Models of Electronic Medical Records

TL;DR

This study applies topic modeling, specifically LDA, to electronic medical records to identify patterns of co-occurring health conditions, aiding diagnosis and understanding of complex patient health profiles.

Contribution

It adapts the LDA model to EMRs for the first time to uncover meaningful patterns of associated health conditions.

Findings

Identified coherent medical condition patterns in EMRs.

LDA topics aligned well with known medical phenomena.

Demonstrated the potential of machine learning in medical pattern discovery.

Abstract

Multiple adverse health conditions co-occurring in a patient are typically associated with poor prognosis and increased office or hospital visits. Developing methods to identify patterns of co-occurring conditions can assist in diagnosis. Thus identifying patterns of associations among co-occurring conditions is of growing interest. In this paper, we report preliminary results from a data-driven study, in which we apply a machine learning method, namely, topic modeling, to electronic medical records, aiming to identify patterns of associated conditions. Specifically, we use the well established latent dirichlet allocation, a method based on the idea that documents can be modeled as a mixture of latent topics, where each topic is a distribution over words. In our study, we adapt the LDA model to identify latent topics in patients' EMRs. We evaluate the performance of our method both qualitatively, and show that the obtained topics indeed align well with distinct medical phenomena characterized by co-occurring conditions.