使用机器学习方法预测维护血液透析患者的社会隔离:一个横截面研究
Ying Li1, Wenwen Zhao2, Boyang Wang3
1College of Sports Science, Jishou University, Jishou, Hunan, China.
Frontiers in psychiatry
|March 6, 2026
概括
使用随机森林的机器学习模型有效地预测了维护血液透析患者的社会隔离. 关键的危险因素包括居住地,心力衰竭和焦虑.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 对于接受维持血液透析 (MHD) 的患者来说,社会隔离是一个重大问题.
- 识别面临风险的人群对于及时干预至关重要.
- 了解关键预测因素可以为有针对性的支持策略提供信息.
研究的目的:
- 开发和验证基于机器学习 (ML) 的风险预测模型,用于MHD患者的社会隔离.
- 在这个群体中确定与社会隔离相关的重大风险因素.
主要方法:
- 实施和比较了七个ML算法:随机森林 (RF),决策树 (DT),K-最近邻居 (KNN),物流回归 (LR),弹性网 (EN),极端梯度增强 (XGB) 和支持向量机器 (SVM).
- 招募了362名MHD患者的队列,并分为训练和检测组.
- 进行特征重要性分析以确定关键预测因素.
主要成果:
- 在MHD队列中,社会孤立的发生率为45.856%.
- 随机森林 (RF) 模型表现出最佳的预测性能,AUC为0.95.
- 发现的重要预测因素包括居住地,心力衰竭 (HF),焦虑,家庭月收入,年龄和睡眠状况.
结论:
- 基于射频的预测模型有效地识别了面临社会隔离风险的MHD患者.
- 这些发现支持临床医生在针对性干预中分层高风险人群.
- 建议在使用较大的队列的多中心研究中进一步验证.
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