在熟练护理设施中预测高尿路感染率:一种机器学习方法
Diane Dolezel1, Tiankai Wang1, Denise Gobert2
1Health Informatics & Information Management Department, Texas State University, Round Rock, TX 78665, USA.
Healthcare (Basel, Switzerland)
|October 29, 2025
概括
机器学习模型可以预测熟练护理设施 (SNFs) 中的尿路感染 (UTI) 风险. 农村SNF和病床数量是关键预测指标,为感染预防策略提供信息.
科学领域:
- 与医疗保健相关的感染
- 传染病流行病学 传染病流行病学
- 机器学习在医疗保健中的应用
背景情况:
- 尿路感染 (UTI) 是在熟练护理设施 (SNFs) 中普遍存在的与医疗保健相关的感染.
- 高的尿路感染率与增加的医疗费用,长时间住院和死亡率有关.
- 需要预测模型来识别高风险的尿路感染的SNF.
研究的目的:
- 开发和评估机器学习模型,用于预测SNF中高UTI率.
- 确定与尿路感染风险增加相关的关键SNF特征.
- 为了告知有针对性的感染预防战略.
主要方法:
- 对94,877个SNF年观测 (2019-2024) 的分析.
- 使用了随机森林,XGBoost和LightGBM机器学习模型.
- 采用Shapley添加式解释用于模型解释和特征重要性.
主要成果:
- 机器学习模型在预测SNF尿路感染方面显著优于后勤回归.
- 随机森林模型确定了农村SNF和工作人员的床位数量作为主要预测指标.
- 平均逗留时间和地理位置也是高尿道感染率的重要预测因素.
结论:
- 设施级特征可以有效地使用机器学习来预测SNF中的UTI风险.
- 识别高风险的SNF允许采取主动的感染预防措施.
- 这些发现支持以数据为导向的方法,以提高急性后护理环境中的患者安全.
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