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基于深度学习的早期预警系统对韩国普通病房的重大不良事件进行前性外部验证
Taeyong Sim1, Eun Young Cho1, Ji-Hyun Kim1
1AITRICS Corp., Seoul, Korea.
Acute and critical care
|June 10, 2025
概括
基于人工智能的早期预警评分 (AI-EWS),即VitalCare - 主要不良事件评分 (VC-MAES),在预测普通病房患者的主要不良事件方面表现出比传统评分更高的准确性. 这种AI-EWS还显著减少了虚假警报,提高了临床效率.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持系统 临床决策支持系统
- 患者监测 患者监测
背景情况:
- 一般病房的急性恶化可能导致主要不良事件 (MAE),如心脏骤停或死亡.
- 传统的早期预警分数 (EWS) 显示出有限的预测准确性和高的错误阳性率.
- 对基于人工智能 (AI) 的EWS进行了外部验证,即VitalCare - 主要不良事件评分 (VC-MAES).
研究的目的:
- 在一般病房患者中对MAE的VC-MAES的预测性能进行外部验证.
- 为了比较VC-MAES与传统的EWS (如NEWS和MEWS) 的准确性.
- 评估VC-MAES对虚假阳性率的影响.
主要方法:
- 一项前性观察性研究包括来自内科和产科/妇科病房的成年患者.
- 将VC-MAES预测与国家预警分数 (NEWS) 和修改预警分数 (MEWS) 进行了比较.
- 使用AUROC,AUPRC和假阳性对真阳性 (FPpTP) 来评估性能.
主要成果:
- 在预测MAE方面,VC-MAES获得了0.918的AUROC和0.352的AUPRC,明显超过了NEWS和MEWS.
- AI-EWS在包括产科和妇科在内的子组中表现强.
- VC-MAES 降低了高达 71% 的虚假警报,同时保持了与 MAE 的强烈关联 (P<0.001).
结论:
- 在预测一般病房患者的不良事件方面,VC-MAES显著优于传统的EWS.
- 基于人工智能的得分提供了更好的临床效率和资源分配潜力.
- VC-MAES表现出强的表现和较低的错误阳性率,表明更广泛的采用效益.
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