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比较单一医院和国家模型来预测30天住院患者的死亡率
Steven Cogill1,2, Kent Heberer1,2, Amit Kaushal3,4
1VA Palo Alto Cooperative Studies Program Coordinating Center, Palo Alto, CA, USA.
Journal of general internal medicine
|January 6, 2025
概括
一个医院自己的死亡率预测模型,使用足够的患者数据,性能与国家人工智能模型相美. 这一发现支持使用本地化模型来评估护理质量,并为临床实践提供信息.
科学领域:
- 医疗保健中的人工智能
- 机器学习用于临床决策支持
- 医疗保健服务研究 医疗服务研究
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 模型越来越多地用于预测患者死亡率.
- 一个关键的问题是,是否使用国家模型或当地医院模型来预测死亡率.
- 这些模型对于评估护理质量和指导临床实践至关重要.
研究的目的:
- 将单一医院30天全因死亡率预测模型的性能与国家基准进行比较.
- 在医疗保健环境中评估局部化与通用化AI模型的有效性.
主要方法:
- 使用来自退伍军人事务部帕洛阿尔托医疗保健系统 (n=9975) 的住院患者数据开发了一个单一医院死亡率预测模型.
- 性能与具有类似预测特征的既定国家模型进行了比较.
- 评估指标包括接收器运营商特征曲线下的区域 (ROC AUC),灵敏度,特异性和平衡的准确性.
主要成果:
- 在使用2720或更多入院的培训组时,在国家和单一医院模型之间没有观察到统计学上显著的性能差异 (ROC AUC 0.89与0.878).
- 单一医院模型的风险评估与国家模型的风险评估或相邻的风险评估一致,92.1%的遭遇.
- 两种模型都将患者分为30天死亡率的五个风险类别.
结论:
- 一个单一的医院死亡率预测模型可以实现与国家模型相比的性能.
- 足够的当地患者数据对于开发有效的单一医院模型至关重要.
- 局部化模型为临床实践中的死亡率预测提供了可行的替代方案.
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