优化老年护理:一个数据驱动的AI模型用于预测老年人使用SHARE数据预测多药风险
Aliaa A Elhosseiny1, Seif Eldawlatly2, Eman Ramadan3
1Institute of Global Health and Human Ecology (I-GHHE), The American University in Cairo, Cairo, Egypt; Department of Pharmacology and Toxicology, Faculty of Pharmacy, The British University in Egypt, Cairo, Egypt.
Neuroscience
|May 7, 2025
概括
多药性 (PP) 在老年人中正在增加. 机器学习模型可以使用纵向数据预测PP风险,强调心理健康是关键因素.
科学领域:
- 老年学与公共卫生
- 计算医学是一种计算医学.
- 医疗保健服务研究 医疗服务研究
背景情况:
- 人口老龄化面临多重疾病,增加医疗保健的复杂性.
- 多药性 (PP),定义为同时使用超过五种药物,是老年人面临的重大挑战.
- PP有助于认知和身体功能下降.
研究的目的:
- 预测50岁以上个体的多药性 (PP) 风险.
- 用纵向数据分析2,4年和6年间隔的PP趋势.
- 确定PP风险的关键预测因素.
主要方法:
- 利用了SHARE研究的数据,重点关注50岁以上的参与者,跨越多个波.
- 使用LASSO回归来选择PP风险的17个关键预测变量.
- 通过交叉验证评估了八个机器学习 (ML) 模型,包括分类提升.
主要成果:
- 多药制药的流行率呈现上升趋势,在研究浪潮中从34.03%增加到39.91%.
- 确定了社会人口统计学,生活方式,身体/精神健康和病史作为关键PP预测因素.
- 分类提升ML模型在预测PP风险方面实现了最高的准确性 (高达75.08%) 和回忆 (高达72.83%).
结论:
- 多药性 (PP) 患病率在老年人中正在上升.
- 纵向数据与机器学习 (ML) 结合,为PP风险预测提供了一种可行的方法.
- 心理健康状况是管理和减轻PP的关键因素.
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