机器学习用于药物错误检测:一个范围审查
Research square
|February 27, 2026
概括
机器学习 (ML) 在检测药物错误方面表现有前途,特别是在处方数据方面. 然而,数据质量和现实世界的验证等挑战需要解决,以便在患者安全方面得到更广泛的应用.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 患者安全研究 患者安全研究
背景情况:
- 药物错误对公共卫生构成重大风险,传统干预措施的成功程度有限.
- 机器学习 (ML) 提供先进的计算方法来提高药物安全性.
- 现有研究强调了ML在识别和预测药物错误方面日益增长的应用.
研究的目的:
- 系统地审查和分类基于ML的方法用于药物错误检测和预测.
- 综合当前的进展,并确定药物安全的ML应用的趋势.
- 突出ML驱动的药物错误分析中的差距和未来方向.
主要方法:
- 在PubMed,Embase和Web of Science (2015年至2025年4月) 进行了全面的文献搜索.
- 研究是根据按照PRISMA-ScR指南预定义的资格标准进行选择的.
- 数据提取使用了一个结构化的框架,由两个独立的审查员进行.
主要成果:
- 22项研究符合纳入标准,揭示了两个主要的ML管道.
- 处方错误检测主要使用基于树模型的结构化数据.
- 使用非结构化的多式联络数据和神经网络来解决药物管理错误.
结论:
- ML显示了药物错误检测的巨大潜力,特别是在处方工作流程中.
- 碎片化的证据,有限的概括性和稀缺的现实世界验证阻碍了当前的ML应用.
- 未来的进步需要高质量的数据集,透明的验证,以及探索各种数据模式,如自由文本.
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