一种用于识别老年痴呆症患者使用的潜在不合适药物的模型:机器学习研究
Qiaozhi Hu1,2, Mengnan Zhao2, Fei Teng3
1Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, China.
International journal of clinical pharmacy
|July 9, 2024
概括
一个新的机器学习模型准确地识别了老年痴呆症患者潜在不合适的药物 (PIM). 这种先进的方法简化了PIM检测,提高了这种脆弱人群的药物安全性.
科学领域:
- 老年学是一门学科.
- 药理学 药理学是指药理学的学科.
- 计算机科学 计算机科学
背景情况:
- 患有痴呆症的老年人存在潜在不适当药物使用 (PIM) 的高风险.
- 由于对标准的不熟悉和处方数据的复杂性,评估PIM具有挑战性.
- 传统的机器学习方法与处方数据的多标签特征作斗争.
研究的目的:
- 开发一种最佳的机器学习模型,用于识别老年痴呆症患者的PIM.
- 结合多个机器学习算法和多标签分类模型.
- 分析处方数据中的相关性,以改善PIM检测.
主要方法:
- 利用集群采样来获取65岁以上痴呆症患者的处方数据 (2020年1月至12月).
- 通过使用2019年Beers标准评估PIM.
- 采用了三种问题转换方法 (二进制相关性,标签权力集,分类链) 与六种分类算法.
主要成果:
- 分析了来自18338名患者的数据,确定了36种PIM类型.
- 分类链+分类增强 (CatBoost) 模型实现了高性能:97.93%的准确性,95.39%的精度,94.07%的回忆率和95.69%的F1得分.
- CC+CatBoost模型显示了低哈明损失 (0.0011) 和高效的运行时间 (371s).
结论:
- 引入了一种新的分类器链+CatBoost (CC+CatBoost) 模型,用于在老年痴呆患者中检测PIM.
- CC+CatBoost模型提供了一种快速而精确的方法来识别PIM.
- 这种机器学习方法简化了手动PIM评估过程.
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