How machine learning on real world clinical data improves adverse event recording for endoscopy.

Journal: NPJ digital medicine
Published Date:

Abstract

Endoscopic interventions are essential for diagnosing and treating gastrointestinal conditions. Accurate and comprehensive documentation is crucial for enhancing patient safety and optimizing clinical outcomes; however, adverse events remain underreported. This study evaluates a machine learning-based approach for systematically detecting endoscopic adverse events from real-world clinical metadata, including structured hospital data such as ICD-codes and procedure timings. Using a random forest classifier detecting adverse events perforation, bleeding, and readmission, we analysed 2490 inpatient cases, achieving significant improvements over baseline prediction accuracy. The model achieved AUC-ROC/AUC-PR values of 0.9/0.69 for perforation, 0.84/0.64 for bleeding, and 0.96/0.9 for readmissions. Results highlight the importance of multiple metadata features for robust predictions. This semi-automated method offers a privacy-preserving tool for identifying documentation discrepancies and enhancing quality control. By integrating metadata analysis, this approach supports better clinical decision-making, quality improvement initiatives, and resource allocation while reducing the risk of missed adverse events in endoscopy.

Authors

  • Stefan Wittlinger
    Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
  • Isabella C Wiest
    Else Kroener Fresenius Center for Digital Health, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany; Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
  • Mahboubeh Jannesari Ladani
    Department of Biomedical Informatics, Mannheim Institute for intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
  • Jakob Nikolas Kather
    Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
  • Matthias P Ebert
    Department of Medicine II, Mannheim Institute for Innate Immunoscience and Clinical Cooperation Unit Healthy Metabolism, Center of Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
  • Fabian Siegel
    Department of Urology and Urosurgery, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; Heinrich-Lanz Center, Department of Biomedical Informatics, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
  • Sebastian Belle
    Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany. Sebastian.belle@umm.de.

Keywords

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