Advances in the application of machine learning in the prediction of adverse drug reactions
- VernacularTitle:机器学习在药物不良反应预测中的应用进展
- Author:
Mengjia XU
1
;
Lin SONG
2
;
Tingting YANG
1
;
Chenrong HUANG
2
Author Information
1. Dept. of Pharmacy，the First Affiliated Hospital of Soochow University，Jiangsu Suzhou 215006，China;College of Pharmaceutical Sciences，Soochow University，Jiangsu Suzhou 215100，China
2. Dept. of Pharmacy，the First Affiliated Hospital of Soochow University，Jiangsu Suzhou 215006，China
- Publication Type:Journal Article
- Keywords:
machine learning;
adverse drug reactions;
prediction;
drug safety;
pharmacovigilance
- From:
China Pharmacy
2026;37(1):105-110
- CountryChina
- Language:Chinese
-
Abstract:
Adverse drug reactions （ADRs） refer to harmful or unintended reactions unrelated to the intended purpose of medication administration， which can lead to various issues such as accelerated disease progression and prolonged hospitalization. Traditional ADRs monitoring systems （such as spontaneous reporting systems） suffer from limitations such as low reporting rates and inconsistent data quality， which hinder the early prevention and control of ADRs. With the rapid development of information technology， machine learning has emerged as a powerful tool for management and decision-making of ADRs by leveraging its strengths in feature extraction and dynamic temporal pattern analysis. By reviewing relevant literature at home and abroad in recent years， this paper summarizes the progress in the application of machine learning for ADRs prediction. It is found that machine learning has gradually been applied to the early warning and risk prediction of ADRs in target organs such as the kidneys， liver， heart and bone marrow （such as acute kidney injury， drug-induced liver injury， and so on）. Although machine learning demonstrates significant application potential in the field of ADRs prediction， it still faces limitations such as inadequate quality control of clinical data， lack of standardized criteria for model performance evaluation， insufficient model interpretability and difficulties in clinical translation. In the future， the development trend of machine learning in the field of ADRs prediction should follow a “technology-validation-integration” pathway to systematically promote the practical implementation of models.
