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 lea...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01826-5 |
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| author | Stefan Wittlinger Isabella C. Wiest Mahboubeh Jannesari Ladani Jakob Nikolas Kather Matthias P. Ebert Fabian Siegel Sebastian Belle |
| author_facet | Stefan Wittlinger Isabella C. Wiest Mahboubeh Jannesari Ladani Jakob Nikolas Kather Matthias P. Ebert Fabian Siegel Sebastian Belle |
| author_sort | Stefan Wittlinger |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-845f83beb32045dc88147f533e32529f |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-845f83beb32045dc88147f533e32529f2025-08-20T03:43:30ZengNature Portfolionpj Digital Medicine2398-63522025-07-018111010.1038/s41746-025-01826-5How machine learning on real world clinical data improves adverse event recording for endoscopyStefan Wittlinger0Isabella C. Wiest1Mahboubeh Jannesari Ladani2Jakob Nikolas Kather3Matthias P. Ebert4Fabian Siegel5Sebastian Belle6Department of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Biomedical Informatics, Mannheim Institute for intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg UniversityElse Kroener Fresenius Center for Digital Health, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of TechnologyDepartment of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Biomedical Informatics, Mannheim Institute for intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg UniversityDepartment of Medicine II, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg UniversityAbstract 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.https://doi.org/10.1038/s41746-025-01826-5 |
| spellingShingle | Stefan Wittlinger Isabella C. Wiest Mahboubeh Jannesari Ladani Jakob Nikolas Kather Matthias P. Ebert Fabian Siegel Sebastian Belle How machine learning on real world clinical data improves adverse event recording for endoscopy npj Digital Medicine |
| title | How machine learning on real world clinical data improves adverse event recording for endoscopy |
| title_full | How machine learning on real world clinical data improves adverse event recording for endoscopy |
| title_fullStr | How machine learning on real world clinical data improves adverse event recording for endoscopy |
| title_full_unstemmed | How machine learning on real world clinical data improves adverse event recording for endoscopy |
| title_short | How machine learning on real world clinical data improves adverse event recording for endoscopy |
| title_sort | how machine learning on real world clinical data improves adverse event recording for endoscopy |
| url | https://doi.org/10.1038/s41746-025-01826-5 |
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