Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis

Qiaozhi Hu; Yuxian Chen; Dan Zou; Zhiyao He; Ting Xu

doi:10.3389/fphar.2024.1497397

Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis

Front Pharmacol. 2024 Nov 13:15:1497397. doi: 10.3389/fphar.2024.1497397. eCollection 2024.

Authors

Qiaozhi Hu^{1

2}, Yuxian Chen¹, Dan Zou¹, Zhiyao He^{1

3}, Ting Xu¹

Affiliations

¹ Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
² West China School of Medicine, Sichuan University, Chengdu, Sichuan, China.
³ 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.

Abstract

Introduction: Adverse drug events (ADEs) pose a significant challenge in current clinical practice. Machine learning (ML) has been increasingly used to predict specific ADEs using electronic health record (EHR) data. This systematic review provides a comprehensive overview of the application of ML in predicting specific ADEs based on EHR data.

Methods: A systematic search of PubMed, Web of Science, Embase, and IEEE Xplore was conducted to identify relevant articles published from the inception to 20 May 2024. Studies that developed ML models for predicting specific ADEs or ADEs associated with particular drugs were included using EHR data.

Results: A total of 59 studies met the inclusion criteria, covering 15 drugs and 15 ADEs. In total, 38 machine learning algorithms were reported, with random forest (RF) being the most frequently used, followed by support vector machine (SVM), eXtreme gradient boosting (XGBoost), decision tree (DT), and light gradient boosting machine (LightGBM). The performance of the ML models was generally strong, with an average area under the curve (AUC) of 76.68% ± 10.73, accuracy of 76.00% ± 11.26, precision of 60.13% ± 24.81, sensitivity of 62.35% ± 20.19, specificity of 75.13% ± 16.60, and an F1 score of 52.60% ± 21.10. The combined sensitivity, specificity, diagnostic odds ratio (DOR), and AUC from the summary receiver operating characteristic (SROC) curve using a random effects model were 0.65 (95% CI: 0.65-0.66), 0.89 (95% CI: 0.89-0.90), 12.11 (95% CI: 8.17-17.95), and 0.8069, respectively. The risk factors associated with different drugs and ADEs varied.

Discussion: Future research should focus on improving standardization, conducting multicenter studies that incorporate diverse data types, and evaluating the impact of artificial intelligence predictive models in real-world clinical settings.

Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024565842, identifier CRD42024565842.

Keywords: adverse drug event; electronic medical record data; machine learning algorithm; prediction model; systematic review.

Publication types

Systematic Review

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Sichuan Science and Technology Support Program (grant number: 2023NSFSC1696), and the Science and Technology project of Chengdu Health Commission (grant number: 2022020). This research was supported by National Key Clinical Specialties Construction Program.