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幕后:比较两个公共保险人群中的预测模型性能
Ruichen Sun1,2, Morgan Henderson2,3, Leigh Goetschius2
1Department of Psychology, University of Maryland Baltimore County, Baltimore, MD.
健康服务利用的预测模型需要针对不同人群进行调整. 在医疗补助和医疗保险组中,可避免住院治疗的模型表现良好,但关键的风险因素差异很大,需要量身定制的方法.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 医疗信息学 医疗信息学
- 预测分析是一种预测分析.
背景情况:
- 预测模型在医疗保健中越来越多地用于预测服务利用率和患者结果.
- 了解这些模型在不同人群中的适应性和上下文功能至关重要,但尚未得到充分研究.
- 本研究研究了两种不同的人口群体的大规模预测模型的内部机制.
研究的目的:
- 检查预测模型在不同人群中可避免住院治疗的适应性.
- 在不同的人口背景下阐明健康预测模型的内部运作和性能变化.
- 强调人口特异性模型培训和适应的重要性.
主要方法:
- 在医疗补助和医疗保险注册人员中比较了可避免住院预测模型的性能和功能.
- 评估风险评分特征,预测准确度和2022年3月两种人群的关键风险因素.
- 通过将医疗保险系数应用于医疗补助人口来评估"不适应"的模型来评估适应的影响.
主要成果:
- 预测模型在医疗补助和医疗保险人口中表现出成功的适应和强的表现.
- 在两个人群之间观察到主要风险因素及其权重的显著差异.
- 与适应模型相比,对医疗补助人口应用的不适应模型表现明显较差.
结论:
- 预测模型需要"视幕后"来理解它们在不同人群中的功能.
- 风险预测不是一个"适合所有人"的解决方案;模型必须根据目标人群量身定制.
- 预测模型的最佳性能需要适应和对预测模型的特定群体进行培训.
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