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
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) |
2206.01436v1
access all versions, variants, and formats of this works (eg, pre-prints)