Modeling electronic health record data using a knowledge-graph-embedded topic model

TL;DR

KG-ETM is a novel knowledge graph-embedded topic model that effectively extracts meaningful disease topics from large-scale EHR data, improving interpretability and clinical utility.

Contribution

Introduces KG-ETM, an end-to-end multimodal model that leverages medical knowledge graphs for better disease topic discovery from EHR data.

Findings

KG-ETM outperforms existing methods in EHR reconstruction.

KG-ETM achieves superior drug imputation accuracy.

The model learns clinically meaningful embeddings for patient stratification.

Abstract

The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic way. However, effective extraction of clinical knowledge from the EHR data has been hindered by its sparsity and noisy information. We present KG-ETM, an end-to-end knowledge graph-based multimodal embedded topic model. KG-ETM distills latent disease topics from EHR data by learning the embedding from the medical knowledge graphs. We applied KG-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on EHR reconstruction and drug imputation. KG-ETM demonstrated superior performance over the alternative methods on both tasks. Moreover, our model learned clinically meaningful graph-informed embedding of the EHR codes. In additional, our model is also able to discover interpretable and accurate patient representations for patient stratification and drug recommendations.