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

Heart Valve Disease Research (6) Clinical Informatics (1444) Natural Language Processing (954) Electronic Health Records (1378) Computer-Aided Diagnosis (278) Cardiac Diagnostics (36)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/60503, first published .
Identifying the Severity of Heart Valve Stenosis and Regurgitation Among a Diverse Population Within an Integrated Health Care System: Natural Language Processing Approach

Identifying the Severity of Heart Valve Stenosis and Regurgitation Among a Diverse Population Within an Integrated Health Care System: Natural Language Processing Approach

Identifying the Severity of Heart Valve Stenosis and Regurgitation Among a Diverse Population Within an Integrated Health Care System: Natural Language Processing Approach

Authors of this article:

Fagen Xie1 Author Orcid Image ;   Ming-sum Lee2 Author Orcid Image ;   Salam Allahwerdy3 Author Orcid Image ;   Darios Getahun1 Author Orcid Image ;   Benjamin Wessler4 Author Orcid Image ;   Wansu Chen1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Bowles A, Lynch J, Bermudez F, Shakt G, DiNatale T, Pridgen K, Judy R, Levin M, Hartmann K, Damrauer S, Alba P. Detecting Bicuspid Aortic Valve From Echocardiographic Reports Using Natural Language Processing. JACC: Advances 2026;5(1):102463 View
  2. Xie F, Lee M, Chen W, Phan D. Converting unstructured cardiac catheterization and echocardiography reports into structured data using transformer-based language models. JAMIA Open 2026;9(2) View