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

by   Yuesong Zou, et al.

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.


page 2

page 14

page 16

page 17


Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph

Increasingly large electronic health records (EHRs) provide an opportuni...

Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation

Clinicians prescribe antibiotics by looking at the patient's health reco...

Knowledge Graph Embedding with Electronic Health Records Data via Latent Graphical Block Model

Due to the increasing adoption of electronic health records (EHR), large...

Supervised multi-specialist topic model with applications on large-scale electronic health record data

Motivation: Electronic health record (EHR) data provides a new venue to ...

A Knowledge Graph-based Approach for Exploring the U.S. Opioid Epidemic

The United States is in the midst of an opioid epidemic with recent esti...

Dark Patterns, Electronic Medical Records, and the Opioid Epidemic

Dark patterns have emerged as a set of methods to exploit cognitive bias...

A latent topic model for mining heterogenous non-randomly missing electronic health records data

Electronic health records (EHR) are rich heterogeneous collection of pat...

Please sign up or login with your details

Forgot password? Click here to reset