Modeling electronic health record data using a knowledge-graph-embedded topic model release_kdaipn7alvfa5lcwlzcfoqfnhq

by Yuesong Zou, Ahmad Pesaranghader, Aman Verma, David Buckeridge, Yue Li

Released as a article .

2022  

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.
In text/plain format

Archived Files and Locations

application/pdf  2.4 MB
file_2sxobhx6tbfl5fjxwbjta22d6y
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2022-06-03
Version   v1
Language   en ?
arXiv  2206.01436v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: f830fd92-7622-468f-a869-eaec9f306070
API URL: JSON