预测和解释社会孤立:在老龄化人口中使用可解释机器学习模型的见解
Sicheng Li1, Kyle Lam2, Jianing Qiu3
1Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China.
The Gerontologist
|December 13, 2025
概括
一个新的可解释的机器学习模型准确地预测了老年人的社会孤立风险. 关键预测因素包括年龄,金融稳定性和环境因素,为干预提供了目标.
科学领域:
- 老年学是指老年学的学科.
- 公共卫生 公共卫生
- 人工智能的人工智能
背景情况:
- 社会孤立影响了四分之一的成年人,并与健康状况不佳有关.
- 现有的社会隔离风险预测模型是不够的.
- 开发准确和可解释的模型对于识别有风险的个体至关重要.
研究的目的:
- 开发和验证可解释的机器学习 (ML) 方法来预测社会隔离.
- 确定中国中年和老年人社会隔离的关键预测因素.
- 探索预测因素和社会隔离之间的潜在因果关系.
主要方法:
- 利用中国健康与退休长度研究 (CHARLS) 的数据进行模型开发.
- 采用了五个ML算法,包括渐变增强机 (GBM),具有283个候选预测器.
- 应用了SHapley添加式扩展 (SHAP) 对于特征重要性和限制立方线 (RCS) 对于因果关联探索.
主要成果:
- GBM模型在预测社会隔离风险方面表现强 (AUC-ROC在开发过程中高达0.767,在外部验证中为0.678).
- 一致的顶级预测因素包括年龄,每月的非食品消费和初级住宅净价值.
- 诸如绿色暴露和社区特征等环境因素也成为重要预测因素.
结论:
- 与现有方法相比,开发的可解释的ML模型,特别是GBM,在识别社会隔离风险方面表现优越.
- 该模型的可解释性突出了可操作和潜在可逆的干预目标.
- 社区和环境层面的因素是缓解社会孤立的关键目标.
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