基于深度学习的早期预警系统在住院患者中存在"蓝色代码"事件的风险和住院时间:回顾性现实世界实施研究
Ji-Hyun Kim1, Eun Young Cho1, Yuhyun Choi1
1AITRICS Corp, 218 Teheran-ro, Gangnam-gu, Seoul, 06221, Republic of Korea, 82 025695507, 82 025695508.
JMIR medical informatics
|August 22, 2025
概括
像VitalCare这样的人工智能 (AI) 系统显著减少了超过24%的医院紧急情况, 这种人工智能预警系统改善了患者的治疗结果和临床医生的支持, 解决了关键的护理挑战.
科学领域:
- 医疗信息学
- 临床决策支持系统
- 医疗保健中的人工智能
背景情况:
- 在超过85%的病例中,需要立即复苏的蓝色事件之前出现了异常的生命体征趋势.
- 持续监测和人工智能驱动的临床数据解释可以防止医院内的关键事件.
研究的目的:
- 在实施基于人工智能的早期预警系统VitalCare后评估临床结果的变化.
- 验证VitalCare重大不良事件评分和死亡评分算法的预测性能.
主要方法:
- 对一般病房和重症监护室30785名住院患者的电子病历进行了回顾性分析.
- 在实施VitalCare之前和之后的3个月的比较分析.
- 测量Code Blue发生率,不良事件,长时间住院和早期干预;对算法性能进行AUROC计算.
主要成果:
- 实施VitalCare导致蓝色代码事件 (P=. 004) 和长时间住院 (P<. 05) 减少了24. 97%.
- 在实施后观察到早期干预率的显著增加.
- 与传统评分系统相比,VitalCare的主要不良事件评分 (AUROC 0. 865) 和死亡率评分 (AUROC 0. 937) 显示出更高的性能.
结论:
- 基于人工智能的高预测能力的模型可以通过提供早期临床见解来预防主要的医院事件.
- 人工智能系统有效应对与人力资源和程序效率相关的医疗保健挑战.
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