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

Natural Language Processing (937) Electronic Health Records (1354) Ontologies, Classifications, and Coding (338) Artificial Intelligence (2252) Registered Report (817)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67837, first published .
Natural Language Processing and ICD-10 Coding for Detecting Bleeding Events in Discharge Summaries: Comparative Cross-Sectional Study

Natural Language Processing and ICD-10 Coding for Detecting Bleeding Events in Discharge Summaries: Comparative Cross-Sectional Study

Natural Language Processing and ICD-10 Coding for Detecting Bleeding Events in Discharge Summaries: Comparative Cross-Sectional Study

Authors of this article:

Frederic Gaspar1, 2 Author Orcid Image ;   Mehdi Zayene3 Author Orcid Image ;   Claire Coumau1, 2, 4 Author Orcid Image ;   Elliott Bertrand3 Author Orcid Image ;   Marie Bettex5 Author Orcid Image ;   Marie Annick Le Pogam5 Author Orcid Image ;   Chantal Csajka2, 4 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Coumau C, Gaspar F, Zayene M, Bertrand E, Alberio L, Lovis C, Beeler P, Rinaldi F, Lutters M, Le Pogam M, Csajka C. Detection of Antithrombotic-Related Bleeding in Older Inpatients: Multicenter Retrospective Study Using Structured and Unstructured Electronic Health Record Data. Journal of Medical Internet Research 2026;28:e77809 View
  2. Wilson K, Riddles J, Hill A, Bassett E, Zhou M, Barron M, Chavez C, Shrivastava R, Battalahalli A, Chacreton D, Moran E, Rowley E, Weber Z, Reichle L, Ball S, Payne A, DeCuir J, Link-Gelles R, Ong T. Identification and validation of respiratory virus immunization using natural language processing. Frontiers in Digital Health 2026;8 View