一个AI-Enabled动态风险分层为急诊室患者与心电图和CXR集成的病人
Yu-Hsuan Jamie Chen1, Chin-Sheng Lin2,3, Chin Lin1,3,4
1School of Public Health, National Defense Medical Center, Taipei, Taiwan.
Journal of medical systems
|July 31, 2023
概括
这项研究介绍了用于紧急部门的人工智能驱动的动态分拣系统. 与传统方法相比,它改善了患者风险分层和死亡率预测,有助于识别被忽视的高风险个体.
科学领域:
- 紧急医疗 紧急医疗
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 目前的急诊室 (ED) 预分系统在准确识别患者预后方面存在局限性,原因是初始评估数据有限.
- 加强风险识别对于ED的及时和适当的患者护理至关重要.
研究的目的:
- 开发和验证使用人工智能 (AI) 的动态分类系统,以提高ED患者风险分层的准确性.
- 整合初始评估数据与心电图 (ECG) 和胸部X射线 (CXR) 发现,以提高预后预测.
主要方法:
- 一项回顾性队列研究,涉及134,112次ED访问 (内部数据) 和45,614次ED访问 (外部验证数据).
- 开发用于初始风险评估的极端梯度增强 (XGB) 模型和用于ECG和CXR分析的两个深度学习模型 (DLM).
- 通过将XGB模型和DLMs的输出结合起来,创建了一个动态分类规模.
主要成果:
- XGB模型实现了接收器操作特征曲线 (AUC) 下的面积>0.866,而ECG和CXR的DLM分别实现了>0.862和>0.886的AUC.
- 基于人工智能的动态分类量表显示,与原始量表 (0.827-0.843) 相比,C指数显著更高 (0.914-0.920).
- 动态尺度显示出对5年死亡率,30天ED复诊和30天出院结果的优异预测能力.
结论:
- 基于人工智能的动态分组为ED患者的死亡风险分层提供了更准确和更适应的方法.
- 这种方法在识别高风险患者时特别有效,因为异常呈现可能会被传统分拣遗漏.
- 人工智能与临床数据的整合提高了预后准确性和紧急情况下的患者管理.
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