Machine learning methods for predicting adverse drug events: A systematic review.

ABSTRACT: Predicting adverse drug events (ADEs) in outpatient settings is crucial for improving medication safety, identifying high-risk patients and reducing health-care costs. While traditional methods struggle with the complexity of health-care data, machine learning (ML) models offer improved prediction capabilities; however, their effectiveness in ADE prediction remains unclear. This systematic review evaluated ML algorithms used for this purpose, analysing studies that focussed on outpatient care or utilized large-scale data sources (e.g. electronic health records, administrative claims and spontaneous reporting systems) that primarily represent the outpatient continuum. We systematically searched MEDLINE and Embase up to December 2024 to identify studies developing or validating ML models for

SUBMITTER: Chalabianloo N 

PROVIDER: S-EPMC12850620 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature