Machine learning to predict adverse drug events based on electronic health records: a systematic review and meta-analysis.

ABSTRACT:

Objective

This systematic review aimed to provide a comprehensive overview of the application of machine learning (ML) in predicting multiple adverse drug events (ADEs) using electronic health record (EHR) data.

Methods

Systematic searches were conducted using PubMed, Web of Science, Embase, and IEEE Xplore from database inception until 21 November 2023. Studies that developed ML models for predicting multiple ADEs based on EHR data were included.

Results

Ten studies met the inclusion criteria. Twenty ML methods were reported, most commonly random forest (RF, n = 9), followed by AdaBoost (n = 4), eXtreme Gradient Boosting (n = 3), and support vector machine (n = 3). The mean area under the summary receiver operator characteristics curve (AUC) was 0.76 (95% confidence

SUBMITTER: Hu Q 

PROVIDER: S-EPMC11639029 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature