机器学习模型用于预测中老年人和老年人的多病症轨迹
Li Yao1,2, Qiaoxing Li3, Zihan Zhou4
1School of Management and Collaborative Innovation Laboratory of Digital Transformation and Governance, Guizhou University, Guiyang, 550025, Guizhou, China.
Scientific reports
|July 9, 2025
概括
预测老龄化人口中的多病态性进展至关重要. 这项研究确定了四个轨迹和关键风险因素,如基线疾病数量和使用机器学习的日常生活能力.
科学领域:
- 老年学是一门学科.
- 公共卫生 公共卫生
- 计算生物学 计算生物学
背景情况:
- 多病症是一个日益严重的公共卫生问题,特别是全球人口老龄化.
- 有效预测和管理老年人多病症进展是必不可少的.
研究的目的:
- 在中年和老年人中开发多病症轨迹的预测模型.
- 确定影响多发性疾病进展的关键因素.
主要方法:
- 利用来自中国健康与退休纵向研究 (CHARLS) 数据库 (12,198名45岁以上的参与者) 的数据.
- 采用时间序列聚类来定义多病态轨迹.
- 开发和评估机器学习模型 (XGBoost,随机森林,SVM,物流回归,ANN) 用于预测.
主要成果:
- 确定了四种不同的多病性进展模式:稳定低风险 (45.26%),逐渐恶化 (14.35%),中等稳定 (31.9%) 和持续恶化 (8.49%).
- XGBoost模型表现出卓越的性能 (精度:0.664,宏观ROC-AUC:0.825).
- 确定的关键预测因素包括基线疾病计数,自我评估的日常生活活动 (ADL) 和自我评估的健康状况.
结论:
- 机器学习模型可以有效地预测老龄化人口中的多病态性轨迹.
- 基线疾病计数,ADL和自我评估的健康状况是预测多病症进展的关键因素.
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