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

Journal: JMIR medical informatics
Published Date: Aug 29, 2025

Abstract

BACKGROUND: Bleeding adverse drug events (ADEs), particularly among older inpatients receiving antithrombotic therapy, represent a major safety concern in hospitals. These events are often underdetected by conventional rule-based systems relying on structured electronic medical record data, such as the ICD-10 (International Statistical Classification of Diseases and Related Health Problems 10th Revision) codes, which lack the granularity to capture nuanced clinical narratives.

Authors

  • Frederic Gaspar
    Center for Research and Innovation in Clinical Pharmaceutical Sciences, Rue du Bugnon 19, Lausanne, 1011, Switzerland, 41 763306834.
  • Mehdi Zayene
    Effixis SA, Lausanne, Switzerland.
  • Claire Coumau
    Center for Research and Innovation in Clinical Pharmaceutical Sciences, Rue du Bugnon 19, Lausanne, 1011, Switzerland, 41 763306834.
  • Elliott Bertrand
    Effixis SA, Lausanne, Switzerland.
  • Marie Bettex
    Department of Epidemiology and Health Systems, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.
  • Marie Annick Le Pogam
    Department of Epidemiology and Health Systems, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.
  • Chantal Csajka
    School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland.

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