This paper is in the following e-collection/theme issue:

Applications of AI (104) Artificial Intelligence (1421)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56932, first published .
Enhancing Clinical Relevance of Pretrained Language Models Through Integration of External Knowledge: Case Study on Cardiovascular Diagnosis From Electronic Health Records

Enhancing Clinical Relevance of Pretrained Language Models Through Integration of External Knowledge: Case Study on Cardiovascular Diagnosis From Electronic Health Records

Enhancing Clinical Relevance of Pretrained Language Models Through Integration of External Knowledge: Case Study on Cardiovascular Diagnosis From Electronic Health Records

Authors of this article:

Qiuhao Lu1, 2, 3 Author Orcid Image ;   Andrew Wen1, 2 Author Orcid Image ;   Thien Nguyen3 Author Orcid Image ;   Hongfang Liu1, 2 Author Orcid Image
Article Authors Cited by (1) Tweetations (1) Metrics

Qiuhao Lu   1, 2, 3 , PhD ;   Andrew Wen   1, 2 , MS ;   Thien Nguyen   3 , PhD ;   Hongfang Liu   1, 2 , PhD

1 McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States

2 Department of AI and Informatics, Mayo Clinic, Rochester, MN, United States

3 Department of Computer Science, University of Oregon, Eugene, OR, United States

Corresponding Author:

  • Hongfang Liu, PhD
  • McWilliams School of Biomedical Informatics
  • University of Texas Health Science Center
  • 7000 Fannin Street
  • Houston, TX, 77030
  • United States
  • Phone: 1 713-500-4472
  • Email: Hongfang.Liu@uth.tmc.edu