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使用可解释的机器学习识别慢性疼痛老年患者的自杀念头:一项横截面研究.

Xiaoang Zhang1,2, Weichen Liu1,2, Daying Zhang2

  • 1School of Nursing, Jiangxi Medical College, Nanchang University, China.

Western journal of nursing research
|November 11, 2025
PubMed
概括

机器学习模型可以识别慢性疼痛的老年人自杀念头 (SI) 风险. 关键因素包括疼痛程度,社会支持和疼痛灾难性,有助于早期发现和干预.

关键词:
年长的年长的老年人慢性疼痛是一种慢性疼痛.机器学习是机器学习.年龄较大的成年人.预测模型 预测模型自杀的想法 自杀的想法

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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.
  • 数据科学数据科学数据科学

背景情况:

  • 在患有慢性疼痛的老年人中,早期识别自杀念头 (SI) 是至关重要的.
  • 在这个群体中存在有限的SI风险识别和分层方法.
  • 机器学习为分析复杂的风险因素关系提供了一种有价值的方法.

研究的目的:

  • 开发可解释的机器学习模型,以识别慢性疼痛的老年人中的SI风险.

主要方法:

  • 一项对中国516名患有慢性疼痛的老年人进行的横截面研究.
  • 数据预处理包括Min-Max规范化和SMOTETomek.
  • 机器学习模型是使用Shapley增量解释来开发和解释的.

主要成果:

  • 一些因素与SI有关,包括疼痛特征和社会支持.
  • 随机森林模型实现了高精度 (0.85) 和AUC (0.89).
  • 疼痛程度,感知到的社会支持和疼痛灾难化是最有影响力的危险因素.

结论:

  • 可解释的机器学习模型有助于早期的SI检测和护士的风险分层.
  • 确定了关键风险因素,支持针对性干预和慢性疼痛老年人IS临床查.