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Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model.
Zou, Yuesong; Pesaranghader, Ahmad; Song, Ziyang; Verma, Aman; Buckeridge, David L; Li, Yue.
Afiliação
  • Zou Y; School of Computer Science, McGill University, Montreal, Canada.
  • Pesaranghader A; School of Computer Science, McGill University, Montreal, Canada.
  • Song Z; School of Computer Science, McGill University, Montreal, Canada.
  • Verma A; School of Population and Global Health, McGill University, Montreal, Canada.
  • Buckeridge DL; School of Population and Global Health, McGill University, Montreal, Canada.
  • Li Y; School of Computer Science, McGill University, Montreal, Canada. yueli@cs.mcgill.ca.
Sci Rep ; 12(1): 17868, 2022 10 25.
Article em En | MEDLINE | ID: mdl-36284225
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 EHR data has been hindered by the sparse and noisy information. We present Graph ATtention-Embedded Topic Model (GAT-ETM), an end-to-end taxonomy-knowledge-graph-based multimodal embedded topic model. GAT-ETM distills latent disease topics from EHR data by learning the embedding from a constructed medical knowledge graph. We applied GAT-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on topic quality, drug imputation, and disease diagnosis prediction. GAT-ETM demonstrated superior performance over the alternative methods on all tasks. Moreover, GAT-ETM learned clinically meaningful graph-informed embedding of the EHR codes and discovered interpretable and accurate patient representations for patient stratification and drug recommendations. GAT-ETM code is available at https://github.com/li-lab-mcgill/GAT-ETM .
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Conhecimento / Registros Eletrônicos de Saúde Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Canadá País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Conhecimento / Registros Eletrônicos de Saúde Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Canadá País de publicação: Reino Unido