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使用机器学习,为住院患者构建掉落风险预测模型.

Cheng-Wei Kang1, Zhao-Kui Yan1, Jia-Liang Tian1

  • 1Department of Orthopaedics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.

BMC public health
|January 20, 2025
PubMed
概括
此摘要是机器生成的。

机器学习准确地预测住院患者的跌倒风险. 随机森林模型确定了关键因素,改善了患者安全和预防策略.

关键词:
偶然的跌倒是由于意外而发生的.住院患者是患者.机器学习是机器学习.模型解释模型解释预测建模的预测建模.风险因素 风险因素

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科学领域:

  • 医疗保健信息学 医疗保健信息学
  • 临床风险管理临床风险管理
  • 机器学习在医学中的应用

背景情况:

  • 住院患者面临严重的跌倒风险,导致不良结果.
  • 准确预测跌倒风险对于有效的预防策略至关重要.

研究的目的:

  • 确定住院患者中跌倒的危险因素.
  • 开发和验证基于机器学习的跌倒风险预测模型.
  • 为了评估各种机器学习算法的性能,用于降落预测.

主要方法:

  • 使用了来自福岛医科大学医院队列研究 (DRYAD数据库) 的数据的横截面设计.
  • 使用合成少数群体过量采样技术与编辑的最近邻居 (SMOTE-ENN) 结合用于数据平衡.
  • 应用单变量分析,LASSO回归和八个机器学习算法,包括随机森林,使用SHAP进行解释性.

主要成果:

  • 随机森林模型表现出强大的预测性能,测试组中的AUC为0.795.
  • 发现的关键预测因素包括ADL (站立,疏散),年龄组,计划手术,轮椅使用,跌倒史,催眠药物,精神药物和远程护理系统.
  • SHAP分析提供了关于这些风险因素的重要性的见解.

结论:

  • 机器学习,特别是随机森林,对于预测住院患者的跌倒风险是有效的.
  • 开发的模型和确定的风险因素可以显著提高患者的安全性,并为医疗保健机构的防摔方案提供信息.