A graph-embedded topic model enables characterization of diverse pain phenotypes among UK Biobank individuals

Abstract

Large biobank repositories of clinical conditions and medications data open opportunities to investigate the phenotypic disease network. To enable systematic investigation of entire structured phenomes, we present graph embedded topic model (GETM). We offer two main methodological contributions in GETM. First, to aid topic inference, we integrate existing biomedical knowledge graph information in the form of pre-trained graph embedding into the embedded topic model. Second, leveraging deep learning techniques, we developed a variational autoencoder framework to infer patient phenotypic mixture. For interpretability, we use a linear decoder to simultaneously infer the bi-modal distributions of the disease conditions and medications. We applied GETM to UK Biobank (UKB) self-reported clinical phenotype data, which contains conditions and medications for 457,461 individuals. Compared to existing methods, GETM demonstrates overall superior performance in imputing missing conditions and medications. Here, we focused on characterizing pain phenotypes recorded in the questionnaire of the UKB individuals. GETM accurately predicts the status of chronic musculoskeletal (CMK) pain, chronic pain by body-site, and non-specific chronic pain using past conditions and medications. Our analyses revealed not only the known pain-related topics but also the surprising predominance of medications and conditions in the cardiovascular category among the most predictive topics across chronic pain phenotypes.

Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model

“…We considered it as a baseline because it has a similar generative process as GAT-ETM but does not utilize knowledge graph. • GETM 28 is an embedded topic model that leverages ICD and ATC medical taxonomy hierarchies by initializing word embedding as the output of node2vec. Note that GETM obtains code embedding on only ICD and ATC taxonomy hierarchies respectively.…”

Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model

“…These recent models are mostly focused on supervision, and their learning algorithms require labelled data. In contrast, GETM [28] leveraged a knowledge graph by combining node2vec [29] with embedded topic model (ETM) [30] in a pipeline approach. GETM is an unsupervised model that directly learn the distribvution of the EHR data using the node2vec embedding.…”

“…• GETM [28] is an embedded topic model that leverages ICD and ATC medical taxonomy hierarchies by initializing word embedding as the output of node2vec. Note that GETM obtain code embedding on only ICD and ATC taxonomy hierarchies respectively.…”