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评估参与基于电话的老年人社会连接干预措施的预测因素:双重机器学习和回归方法

Minji Chae¹, Arlette Chavez¹, Maya Singh¹

¹Humana Integrated Health Systems Sciences Institute, Houston, TX, USA.

Gerontology & geriatric medicine

|October 2, 2023

概括

社会上孤立的残疾,抑郁或关节炎的老年人更有可能加入基于电话的社会项目. 识别这些参与者有助于改善外展和参与.

科学领域:

老年学是指老年学的学科.
公共卫生公共卫生
计算社会科学计算社会科学

背景情况:

社会隔离严重影响老年人的精神和身体健康.
促进社会联系的干预措施为健康带来了巨大的好处.
了解参与驱动因素对于有效的项目交付至关重要.

研究的目的:

确定参与基于电话的社会连接干预的关键预测因素.
分析影响65岁以上社会孤立老年人参与的因素.

主要方法:

采用机器学习 (k-最近邻居,决策树,梯度增强树,随机森林) 和回归分析.
通过分析来自社会连接干预计划的数据,确定了重要的预测因素.
验证的模型性能,测试准确度超过95%.

主要成果:

机器学习模型实现了高分辨能力 (准确率>95%).
参与的关键预测因素包括残疾,抑郁,关节炎和更高的风险得分.
干预措施对特定的高风险老年人群具有吸引力.

结论:

基于电话的社会联系干预措施有效地接触到具有特定健康状况和更高风险概况的老年人.
识别参与预测因素对于提高此类计划的覆盖范围和参与度至关重要.
这项研究为优化弱势老年人群的社会干预策略提供了基础.

关键词:

医疗服务研究医疗服务研究机器学习是机器学习.年龄较大的成年人.这是一个心理社会心理社会问题.社会连接性社会连接性健康的社会决定因素社会隔离,社会隔离.

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