合规选择性预测与成本意识的推迟安全的临床选在分配轮班下
1Department of Artificial Intelligence and Data Science, Korea Military Academy, Seoul, 01805, South Korea.
Scientific reports
|February 20, 2026
概括
本研究引入了一种选择性预测框架,用于使用符合性预测和成本意识的推迟进行临床选. 它通过减少错误并提供可靠的决策支持来提高患者的安全性,即使数据分布发生变化.
科学领域:
- 临床决策支持系统临床决策支持系统
- 机器学习在医疗保健中的应用
- 不确定性量化不确定性的量化.
背景情况:
- 临床分组需要准确的患者优先级,以便及时干预.
- 现有系统可能会在数据转移和量化预测不确定性方面扎.
- 确保患者安全需要强大的决策支持工具.
研究的目的:
- 开发一个临床选选择性预测框架.
- 整合校准的概率建模,符合性预测和成本意识的延期.
- 通过控制预测不确定性和推迟低可信度病例来提高患者安全.
主要方法:
- 用于与覆盖控制的设定值预测的分割合规预测.
- 根据性别分层覆盖范围的蒙德里安变体,以雇佣群体为条件.
- 在分布转移下应用重要度加权变体的稳定性.
- 通过将校准集的预期成本降至最低来选择延期值.
主要成果:
- 选择性推迟将误差降低了49.6% (在分发中) 和46.7% (在分发之外),而败血症预测的覆盖率为80%.
- 实现了有利的风险覆盖权衡,预计成本低.
- 证明了强大的校准和保守的排除性能,具有近乎完美的负预测值.
- 蒙德里安方法将性别覆盖率差距降至1.4个百分点.
结论:
- 合规的不确定性量化与成本意识的推迟提供了透明和更安全的临床决策支持.
- 框架在时间分布转移下优雅地降解.
- 这种方法提高了临床分拣系统的可靠性和安全性.
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