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机器学习模型预测住院患者跌倒的情况
Hojjat Salehinejad1,2, Ricky Rojas1, Kingsley Iheasirim3
1Kern Center for the Science of Health Care Delivery, Division of Healthcare Delivery Research, Mayo Clinic College of Medicine and Science, 200 First St SW, Rochester, MN, 55905, USA.
Scientific reports
|November 25, 2025
概括
与赫斯特-戴维斯分数相比,机器学习模型显著改善了住院患者的跌倒风险预测. 使用患者数据的先进模型为患者安全计划提供了更高的准确性.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 临床预测模型临床预测模型
- 患者安全 患者安全
背景情况:
- 住院患者跌倒对患者安全构成重大风险,并可能导致伤害.
- 赫斯特-戴维斯评分 (HD) 是评估跌倒风险的常用工具,其预测准确性有限.
- 迫切需要更精确的方法来预测住院患者的跌倒.
研究的目的:
- 开发和评估动态机器学习模型,用于预测住院患者的跌倒风险.
- 将机器学习模型的性能与传统的赫斯特-戴维斯分数进行比较.
- 用全面的患者数据确定住院患者倒的关键预测因素.
主要方法:
- 来自17家医院的46695名患者 (2018年1月至2022年7月) 的回顾性分析.
- 开发四种动态机器学习模型,包括赫斯特-戴维斯变量,社会人口统计学,并发症,生理测量,药物和时间序列数据.
- 模型每8小时和24小时更新一次.
主要成果:
- 极端梯度增强 (EGB) 模型实现了0.87的曲线下面积 (AUC) (95% CI 0.86-0.88).
- 赫斯特-戴维斯得分显示AUC显著较低:0.57 (95% CI 0.56-0.58) 和0.62 (95% CI 0.59-0.61).
- 发现的关键预测因素包括神经疾病,行为异常,氧和,心率和IV罗塞米德的使用.
结论:
- 与赫斯特-戴维斯分数相比,机器学习模型,特别是EGB,在预测住院患者跌倒方面提供了更高的准确性.
- 集成多种患者数据的动态模型显示出改善临床跌倒风险评估的前景.
- 对于这些先进的预测工具的临床实施,需要进一步的前性验证.
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