Enhancing Phenotype Discovery in Electronic Health Records through Prior Knowledge-Guided Unsupervised Learning

TL;DR

This paper introduces a Bayesian latent class framework that incorporates clinical knowledge into unsupervised learning of EHR data, enhancing interpretability and identifying meaningful disease sub-phenotypes, exemplified by a T2 inflammation-related asthma subgroup.

Contribution

It presents a novel method for integrating domain-specific knowledge into Bayesian clustering of EHR data, improving phenotype discovery and interpretability.

Findings

Identified a distinct T2 inflammation-related asthma sub-phenotype.

Demonstrated the model's ability to handle missing data and uncertainty.

Revealed a bimodal distribution indicating clear class separation.

Abstract

Objectives: Unsupervised learning with electronic health record (EHR) data has shown promise for phenotype discovery, but approaches typically disregard existing clinical information, limiting interpretability. We operationalize a Bayesian latent class framework for phenotyping that incorporates domain-specific knowledge to improve clinical meaningfulness of EHR-derived phenotypes and illustrate its utility by identifying an asthma sub-phenotype informed by features of Type 2 (T2) inflammation. Materials and methods: We illustrate a framework for incorporating clinical knowledge into a Bayesian latent class model via informative priors to guide unsupervised clustering toward clinically relevant subgroups. This approach models missingness, accounting for potential missing-not-at-random patterns, and provides patient-level probabilities for phenotype assignment with uncertainty. Using reusable and flexible code, we applied the model to a large asthma EHR cohort, specifying informative priors for T2 inflammation-related features and weakly informative priors for other clinical variables, allowing the data to inform posterior distributions. Results and Conclusion: Using encounter data from January 2017 to February 2024 for 44,642 adult asthma patients, we found a bimodal posterior distribution of phenotype assignment, indicating clear class separation. The T2 inflammation-informed class (38.7%) was characterized by elevated eosinophil levels and allergy markers, plus high healthcare utilization and medication use, despite weakly informative priors on the latter variables. These patterns suggest an "uncontrolled T2-high" sub-phenotype. This demonstrates how our Bayesian latent class modeling approach supports hypothesis generation and cohort identification in EHR-based studies of heterogeneous diseases without well-established phenotype definitions.