通过人工智能预测紧急部门的骚动事件
Ambrose H Wong1, Atharva V Sapre1, Kaicheng Wang2
1Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut.
JAMA network open
|May 7, 2025
概括
紧急部门的动荡正在上升,构成安全风险. 一个新的AI模型使用电子健康记录准确预测激动事件,从而实现预防性干预和改善患者护理.
科学领域:
- 紧急医疗 紧急医疗
- 医疗保健中的人工智能
- 临床预测建模临床预测建模
背景情况:
- 在急诊室 (ED) 中,激动事件正在增加,对患者和医疗保健提供者构成重大安全风险.
- 兴奋的复杂性,与不同的临床原因和行为模式,使准确的预测在紧急情况下具有挑战性.
研究的目的:
- 开发,培训和验证专门设计用于预测激动事件的人工智能 (AI) 模型.
- 该模型利用过去紧急部门 (ED) 访问的大量和多样化的数据集来确定预测因素.
主要方法:
- 一项回顾性队列研究利用了一个大型城市卫生系统内的9个ED站点的电子健康记录 (EHR) 数据.
- 分析了2015年至2022年期间超过300万次ED访问 (年龄在18岁以上的患者) 的数据.
- 主要结果,激动,是由肌肉内化学镇静或暴力身体克制的命令定义的. 模型性能使用AUROC和PR-AUC指标进行评估.
主要成果:
- 最终的AI模型结合了50个预测因素,并在交叉验证中显示出强大的预测性能,AUROC为0.94和PR-AUC为0.41.
- 关键预测因素包括之前的ED访问频率,初始生命体征,病史,首席投诉以及之前的镇静/限制事件.
- 模型校准在预测的概率范围内强大,表明预测激动风险的可靠性.
结论:
- 开发的预测模型准确地识别了ED中患有激动风险的患者,显示出高准确性和广泛适用于不同人群.
- 该模型的临床实施可以促进主动降级策略,并可能防止激动事件.
- 这种方法支持通过实现及时和有针对性的干预来加强以患者为中心的护理.
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