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早期预测创伤患者的输血需求,使用可解释的机器学习

Michael R De La Rosa¹, Alexander Bowers², Brian Eastridge²

¹Joe R. & Teresa Lozano Long School of Medicine, University of Texas San Antonio, San Antonio, Texas, USA.

|February 16, 2026

概括

机器学习模型可以快速识别需要输血的创伤患者. XGBoost模型显示出强大的预测性能,有可能改善出血性休克病例的生存率.

科学领域:

创伤护理的护理人员.
医疗信息学医学信息学
机器学习应用程序机器学习应用程序

背景情况:

出血性休克是创伤患者可预防死亡的主要原因.
早期输血显著提高了生存率.
迅速识别需要输血的患者至关重要.

研究的目的:

开发和验证机器学习模型,以预测创伤患者需要输血的需要.
通过更早地识别高风险患者,缩短最初输血的时间.
为了评估各种机器学习算法对此任务的性能.

主要方法:

使用创伤质量改善计划 (TQIP) 数据集 (2019-2022) 的回顾性研究.
包括成人创伤患者;不包括缺乏输血数据的患者.
评估了后勤回归,回归,LASSO,随机森林和XGBoost模型.
使用了80/20列车测试分割和五倍交叉验证来进行超参数调整.
使用接收器操作特征曲线 (AUROC) 下面面积的优化模型.

主要成果:

包括1,232,956名患者;8.9%接受了输血.
XGBoost实现了最高的AUROC,为0.8452.
在最大化平衡精度的门上,XGBoost的精度为72.98%,灵敏度为79.27%,特异性为72.36%.
沙普利添加式扩张 (SHAP) 确定了损伤严重程度得分,静脉血压,激活水平,脉率和年龄作为关键预测因素.

结论:

XGBoost仅使用到达时间变量表现出强大的预测能力.
SHAP分析为模型预测提供了临床上有意义的见解.
XGBoost模型显示出作为输血决策的快速临床决策支持工具的潜力.
外部验证是必要的,以确认可通用性.

关键词:

在XGBoost上使用.临床决策支持临床决策支持可解释的人工智能机器学习是机器学习.输血是输血的方法之一.创伤的创伤创伤的创伤.

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