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机器学习在药物不良反应预测中的应用进展
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| 篇名： | 机器学习在药物不良反应预测中的应用进展 |
| TITLE： | Advances in the application of machine learning in the prediction of adverse drug reactions |
| 摘要： | 药物不良反应（ADRs）是药物出现有害的或与用药目的无关的反应，可导致疾病进程加快、患者住院时间延长等诸多问题。传统ADRs监测（如自发呈报系统）存在上报率低、数据质量参差不齐等问题，这制约了ADRs的早期防控。随着信息技术的飞速发展，机器学习凭借其强大的特征挖掘能力和动态时序分析能力，为临床ADRs的管理与决策提供了强有力的支持。本文通过梳理近年来国内外的相关文献，对机器学习在ADRs预测中的应用进展进行了归纳总结。结果显示，机器学习已逐渐应用于肾脏、肝脏、心脏及骨髓等靶器官ADRs（如急性肾损伤、药物性肝损伤等）的早期预警和风险预测；虽然机器学习在ADRs预测领域表现出巨大的应用潜力，但是仍存在临床数据质量控制不足、模型性能评价标准缺失、模型可解释性不足与临床转化困难等局限。未来，机器学习在ADRs预测领域的发展趋势应遵循“技术-验证-整合”途径，系统性地推动模型落地。 |
| 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. |
| 期刊： | 2026年第37卷第01期 |
| 作者： | 许梦佳；宋林；杨婷婷；黄晨蓉 |
| AUTHORS： | XU Mengjia，SONG Lin，YANG Tingting，HUANG Chenrong |
| 关键字： | 机器学习；药物不良反应；预测；药物安全；药物警戒 |
| KEYWORDS： | machine learning； adverse drug reactions； prediction； drug safety； pharmacovigilance |
| 阅读数： | 47 次 |
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