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

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.