ArticleiScience2022
A graph-embedded topic model enables characterization of diverse pain phenotypes among UK biobank individuals.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A computational genetic- and transcriptomics-based study nominates drug repurposing candidates for the treatment of chronic pain.EBioMedicine · 2025Article
- A methodological framework for integrating model-guided medicine and multidimensional information management systems: application in anti-aging healthcare.International journal of computer assisted radiology and surgery · 2025Article
- FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting.NPJ digital medicine · 2025Article
- A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future.Aging clinical and experimental research · 2023Review
- Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank.Cell genomics · 2023Article
- Clinical Feature Vector Generation using Unsupervised Graph Representation Learning from Heterogeneous Medical Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model.Scientific reports · 2022Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Large 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.