在多个医院设置中的不确定性意识停留时间预测的层次性合规框架
Marzieh Amiri Shahbazi1, Ali Baheri2, Nasibeh Azadeh-Fard2
1Kate Gleason College of Engineering, Rochester Institute of Technology, 1 Lomb Memorial Drive, Rochester, NY, 14623, USA. ma7684@rit.edu.
Scientific reports
|January 29, 2026
概括
本研究引入了一种混合框架,用于对医疗保健数据的不确定性量化. 它提高了预测间隔的准确性和患者结果的覆盖率,有助于资源配置.
科学领域:
- 医疗保健分析 医疗保健分析
- 统计建模 统计建模
- 机器学习 机器学习
背景情况:
- 在等级医疗保健数据中量化不确定性是具有挑战性的.
- 现有的方法,如合规预测和贝叶斯方法,在覆盖范围和精度上有局限性.
- 层次数据结构需要专门的不确定性量化技术.
研究的目的:
- 开发一种混合框架,将符合性预测和贝叶斯方法结合起来,用于层次化的医疗保健数据.
- 为了提高预测间隔的适应性和可靠性.
- 为了解决覆盖保证和间隔精度之间的权衡问题.
主要方法:
- 提出了对等级随机森林的混合框架.
- 集成群体意识的合规校准与贝叶斯后部不确定性.
- 利用跨医院的聚合校准进行大规模验证.
- 在大型患者数据集上使用停留时间预测来评估表现.
主要成果:
- 通过适应性预测间隔实现了接近名义的经验覆盖率 (94.3%).
- 证明了适应区间宽度的重新分配,在低不确定性情况下更窄,在高不确定性情况下更宽.
- 与标准合规预测和未校准的贝叶斯方法相比,表现得更好.
- 通过分组交叉验证,确认了对未见机构的概括.
结论:
- 混合框架有效地平衡了层次医疗数据的覆盖范围和准确性.
- 这种方法支持基于证据的资源配置和风险分层的患者管理.
- 聚合校准提供了一个可扩展的解决方案,没有特定站点的重新校准.
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