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开发和验证一种机器学习模型,用于对老年中风患者潜在不合适药物的风险

Xiaodan Yang¹, Qianqian Ye¹, Mengxiang Zhang¹

¹Department of Pharmacy, The second Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China.

Frontiers in pharmacology

|June 9, 2025

概括

一个机器学习模型准确地预测了老年中风患者可能不适合的药物 (PIM). 这种工具有助于临床医生识别高风险个体,提高药物安全性和合理使用.

科学领域:

老年医学老年医学
临床药房临床药房
医疗信息学医疗信息学

背景情况:

潜在不合适的药物 (PIM) 对老年中风患者构成重大风险.
需要有效的风险预测模型来指导合理的临床药物使用.

研究的目的:

开发和验证基于机器学习的风险预测模型,用于老年中风患者的PIM.
为识别高风险患者和优化药物管理提供决策支持.

主要方法:

利用来自1,252名老年中风患者的数据,使用2023年的酒标准评估PIM.
开发并比较了四种机器学习模型:随机森林,弹性网 (Enet),支持矢量机器分类器和极端梯度提升.
使用内部 (7:3 分割) 和外部 (240 个患者) 队列的验证模型,评估歧视,校准和临床效用.

主要成果:

弹性网 (Enet) 模型在内部和外部验证中表现出卓越的性能.
在外部验证集中,Enet在曲线下的面积 (AUC) 达到0.894.
该模型表现出优异的校准和显著的临床实用性,跨越了广泛的概率值.

结论:

开发的Enet预测模型准确地识别了老年中风患者的PIM风险.
该模型显示出良好的准确性和通用性,支持临床决策.
这种工具可以促进有针对性的干预措施,以预防和减少PIM在这个弱势群体.

关键词:

年长的病人,老年患者.机器学习就是机器学习.可能不合适的药物治疗.预测模型预测模型一次性中风中风中风中风中风

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