biostudies-literatureUnknown18(1)Murphy RMTo reduce adverse drug events (ADEs), hospitals need a system to support them in monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing (NLP), a computerized approach to analyze text data, has shown promising results for the purpose of ADE detection in the context of pharmacovigilance. However, a detailed qualitative assessment and critical appraisal of NLP methods for ADE detection in the context of ADE monitoring in hospitals is lacking. Therefore, we have conducted a scoping review to close this knowledge gap, and to provide directions for future research and practice. We included articles where NLP was applied to detect ADEs in clinical narratives within electronic health records of inpatients. Quantitative and qualitative data items relating to NLP metPloS onee0279842https://www.ebi.ac.uk/biostudies/studies/S-EPMC9810201biostudies-literatureAdverse drug event detection using natural language processing: A scoping review of supervised learning methods.PMC9810201de Keizer NFMurphy RMLeopold JHAbu-Hanna AKlopotowska JESchut MCJager KJDongelmans DAfalseAdverse drug event detection using natural language processing: A scoping review of supervised learning methods.To reduce adverse drug events (ADEs), hospitals need a system to support them in monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing (NLP), a computerized approach to analyze text data, has shown promising results for the purpose of ADE detection in the context of pharmacovigilance. However, a detailed qualitative assessment and critical appraisal of NLP methods for ADE detection in the context of ADE monitoring in hospitals is lacking. Therefore, we have conducted a scoping review to close this knowledge gap, and to provide directions for future research and practice. We included articles where NLP was applied to detect ADEs in clinical narratives within electronic health records of inpatients. Quantitative and qualitative data items relating to NLP met2023-01-01T00:00:00Z20232025-04-19T04:45:09.312Z2025-04-19T04:45:09.312ZS-EPMC98102013659551710.1371/journal.pone.0279842