文章基本信息

标题：A graph-embedded topic model enables characterization of diverse pain phenotypes among UK biobank individuals
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作者：Yuening Wang ; Rodrigo Benavides ; Luda Diatchenko 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2022
卷号：25
期号：6
页码：1-27
DOI：10.1016/j.isci.2022.104390
语种：English
出版社：Elsevier
摘要：SummaryLarge biobank repositories of clinical conditions and medications data open opportunities to investigate the phenotypic disease network. We present a graph embedded topic model (GETM). We integrate existing biomedical knowledge graph information in the form of pre-trained graph embedding into the embedded topic model. Via a variational autoencoder framework, we infer patient phenotypic mixture by modeling multi-modal discrete patient medical records. We applied GETM to UK Biobank (UKB) self-reported clinical phenotype data, which contains 443 self-reported medical conditions and 802 medications for 457,461 individuals. Compared to existing methods, GETM demonstrates good imputation performance. With a more focused application on characterizing pain phenotypes, we observe that GETM-inferred phenotypes not only accurately predict the status of chronic musculoskeletal (CMK) pain but also reveal known pain-related topics. Intriguingly, medications and conditions in the cardiovascular category are enriched among the most predictive topics of chronic pain.Graphical abstractDisplay OmittedHighlights•Interpretable deep learning to integrate knowledge graphs and patient data•Modeling phenotypes from self-reports of 457,461 individuals from the UK Biobank•Predicting and characterizing chronic pain phenotypes using latent phenotypes•Potential link between cardiovascular conditions or medications and chronic painHealth sciences; General medicine; Bioinformatics; Medical informatics