开发一种机器学习风险调整方法,用于痴呆症老人院居民的住院和急诊室访问
Huiwen Xu1,2, John R Bowblis3,4, Adan Z Becerra5
1School of Public and Population Health, University of Texas Medical Branch.
Medical care
|July 13, 2023
概括
像XGBoost和物流回归这样的机器学习模型可以有效地预测阿尔茨海默病和相关痴呆症 (ADRD) 的养老院居民的住院和急诊室访问. 这两种方法都提供了可比的风险调整利率,有助于质量评估.
科学领域:
- 老年学是一门学科.
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 患有阿尔茨海默病和相关痴呆症 (ADRD) 的长期住院住户面临住院转移的高风险.
- 养老院现有的风险调整方法可能需要改进.
研究的目的:
- 开发和比较风险调整的住院率和急诊室 (ED) 长期住宿养老院居民与ADRD的访问.
- 评估极端梯度增强 (XGBoost) 和后勤回归在预测这些事件中的性能.
主要方法:
- 国家医疗保险申请和养老院评估数据的二次分析.
- 利用XGBoost和58个预测器的物流回归模型来预测住院和急救诊所的访问.
- 使用这两种建模技术构建并比较了NH级风险调整利率.
主要成果:
- 与后勤回归相比,XGBoost的预测性能略高 (入院时AUC为0.88与0.86;急救诊所访问时AUC为0.85与0.83).
- 住院和急救诊所的风险调整率在XGBoost和后勤回归之间是可比的.
- 拥有较高比例的ADRD居民和更好的注册护士与工作人员比率的养老院显示较低的调整率.
结论:
- 无论是XGBoost还是后勤回归,都为患有ADRD的养老院居民的风险调整住院和ED访问率提供了可比的估计.
- 这些机器学习方法可以提高养老院的风险调整方法.
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