在重大事件中进行分选:开发和外部验证新型机器学习衍生的初级和二级分选工具
Yuanwei Xu1,2, Nabeela Malik3,4,5,6, Saisakul Chernbumroong1,7
1Centre for Computational Biology, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK.
Emergency medicine journal : EMJ
|September 26, 2023
概括
机器学习开发了新的分类工具,这些工具在重大事件后显著优于现有的方法来识别严重受伤的患者. 这些先进的工具为挽救生命的干预提供了更好的灵敏度和准确性.
科学领域:
- 紧急医疗 紧急医疗
- 在医疗保健中的数据科学.
- 创伤护理 创伤护理
背景情况:
- 重大事件 (MIs) 会导致严重的死亡和残疾,需要对时间关键干预进行有效的分拣.
- 现有的分类工具,通常基于专家意见,缺乏强有力的证据证明其有效性.
- 这项研究解决了通过开发和验证新型机器学习 (ML) 模型来改善分拣的需求.
研究的目的:
- 开发和验证机器学习模型,用于用于重大事件患者的新型初级和二级分拣工具.
- 将ML衍生工具的性能与现有的分类方法进行比较.
- 确定确定优先级1 (P1) 患者需要立即救命干预的关键预测因素.
主要方法:
- 利用英国创伤审计和研究网络 (TARN) 登记 (2008-2017) 使用预定义的标准来识别P1患者.
- 训练并测试了各种ML模型 (随机森林,XGBoost,后勤回归,决策树) 在一个按时间划分的数据集 (70:30) 上.
- 与已建立的工具 (BCD选,CareFlight,MPTT-24,MSTART,NARU选,RAMP) 进行了ML模型的比较,并在联合剧院创伤登记 (JTTR) 数据上外部验证了最佳二级模型.
主要成果:
- 现有的表现最好的工具,BCD选,灵敏度为68.2%,AUC为0.688.
- 结合无法呼吸,胸部受伤和精神状态的决策树模型作为候选主要工具获得了卓越的性能 (灵敏度73.0%,AUC 0.782).
- 建议的二次工具,添加伤害机制,显示出高性能 (灵敏度77.9%,AUC 0.817) 和有利的外部验证 (灵敏度97.6%,AUC 0.778).
结论:
- 机器学习衍生的分类工具在全国创伤人口中显著优于现有方法.
- 这种新的初级分拣工具在临床实施之前需要进一步的外部验证.
- 二级分拣工具显示出强大的外部有效性和在重大事件中支持临床决策的潜力.
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