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

Autor:	Hu Q; Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.; West China School of Medicine, Sichuan University, Chengdu, Sichuan, China., Li J; Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China., Li X; West China School of Medicine, Sichuan University, Chengdu, Sichuan, China., Zou D; Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, Sichuan, China., Xu T; Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, Sichuan, China., He Z; Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.; Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China.
Jazyk:	angličtina
Zdroj:	The Journal of international medical research [J Int Med Res] 2024 Dec; Vol. 52 (12), pp. 3000605241302304.
DOI:	10.1177/03000605241302304
Abstrakt:	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 interval [CI] = 0.26-0.95). RF combined with resampling-based approaches achieved high AUCs (0.9448-0.9457). The common risk factors of ADEs included the length of hospital stay, number of prescribed drugs, and admission type. The pooled estimated AUC was 0.72 (95% CI = 0.68-0.75). Conclusions: Future studies should adhere to more rigorous reporting standards and consider new ML methods to facilitate the application of ML models in clinical practice. Competing Interests: Declaration of conflicting interestThe authors declare that there is no conflict of interest.
Databáze:	MEDLINE
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