可部署的基于机器学习的决策支持系统,用于急性烧伤患者的气管切除术
Haisheng Li1, Ni Zhen1, Shixu Lin2
1Institute of Burn Research, Southwest Hospital, State Key Laboratory of Trauma and Chemical Poisoning, Third Military Medical University (Army Medical University), Chongqing 400038, China.
Burns & trauma
|May 14, 2025
概括
这项研究开发了一种机器学习系统,用于预测烧伤患者的气管切除术需求,改善紧急呼吸道管理. 经过验证的工具,包括名录和计算器,提供可靠的决策支持,特别是在资源有限的环境中.
科学领域:
- 医疗信息学 医疗信息学
- 紧急医疗 紧急医疗
- 手术重症监护手术重症监护
背景情况:
- 呼吸道阻塞是急性烧伤的关键紧急情况,通常需要气管切除术以使呼吸道通透和机械通风.
- 燃烧患者气管切除术的指示具有挑战性和争议性,导致潜在的延迟或不必要的程序.
- 开发可靠的决策支持系统对于优化急性烧伤护理中气管切除术决策至关重要.
研究的目的:
- 开发和验证可部署的基于机器学习 (ML) 的决策支持系统,用于预测急性烧伤患者气管切除术的必要性.
- 通过名ogram,在线计算器和缩写尺度来增强ML模型的解释性和临床实用性.
- 评估开发系统在不同临床环境中的通用性和预测性能.
主要方法:
- 从西南医院 (2018-20年) 的1011名烧伤患者的队列使用开发和特征选择.
- 在独立的内部跨时间队列 (n=274,2021) 和外部跨机构队列 (n=376,2020-21) 上进行验证.
- 使用了极端梯度提升 (XGB) 分类器,构建了一个基于ML的名ogram,在线计算器和缩写尺度 (BETS).
主要成果:
- XGB模型实现了高预测性能,在训练中AUROC为0.973,在两个验证队列中>0.95.
- 该模型通过性别,年龄,烧伤区域和吸入伤害分层的子组表现出稳定的歧视能力 (AUROC 0.903-0.990).
- 基于ML的名图,BETS尺度和在线计算器显示出基于校准和决策曲线分析的可行性和可靠性.
结论:
- 开发的基于ML的决策支持系统表现出强大的预测能力和可概括性,用于急性烧伤患者的气管切除术的必要性.
- 诺米图,在线计算器和缩写尺度提供了可比的预测性能,可以在各种临床场景中部署.
- 该系统提供了宝贵的决策支持,特别是在资源有限的临床环境中,以优化燃烧幸存者的紧急气道管理.
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