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开发一种可解释的机器学习模型,用于选功能障碍的老年人抑郁症.

Deyan Liu1, Yuge Tian1, Min Liu2

  • 1School of Physical Education, Shandong University, Jinan 250061, China.

Journal of affective disorders
|March 6, 2025
PubMed
概括

机器学习模型有效预测功能障碍的老年人抑郁症. 关键因素包括睡眠,年龄,认知,健康状况和生活方式,有助于早期识别和管理.

关键词:
抑郁症 抑郁症 抑郁症机器学习 机器学习这个名字叫做Nomogram.具有功能障碍的老年人.风险预测模型的风险预测模型.解释SHAP的解释

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科学领域:

  • 老年学是一门学科.
  • 精神病学是一个精神病学.
  • 计算医学是一种计算医学.

背景情况:

  • 抑郁症是老年人,特别是那些有功能障碍的人群的重大公共卫生问题.
  • 确定可靠的预测因素和开发准确的风险评估工具对于及时干预至关重要.

研究的目的:

  • 开发和验证基于机器学习的抑郁症风险预测模型,用于功能障碍的老年人.
  • 在这个弱势群体中确定抑郁症的关键预测因素.

主要方法:

  • 利用了2020年中国健康与退休长度研究中的4322名参与者 (60岁以上) 的数据.
  • 采用LASSO,单变量和多变量逻辑回归来识别预测因素.
  • 构建并评估了五种机器学习模型:逻辑回归,随机森林,梯度提升,K-最近邻居和天真贝斯.

主要成果:

  • 重要的预测因素包括睡眠时间,年龄,认知评分,性别,居住区,自我评估的健康状况,关节炎,胃肠道疾病,退休状态,生活满意度,复合疼痛和体力活动.
  • 梯度增强 (AUC:0.76) 和物流回归 (AUC:0.75) 模型显示出强大的预测性能.
  • SHAP解释和名ogram可视化增强了模型的可解释性.

结论:

  • 机器学习模型为预测功能障碍的老年人抑郁风险提供了有价值的工具.
  • 这些模型可以支持社区查和针对性干预的临床决策.