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机器学习方法的评估,以预测创伤中的大规模输血

Matt Strickland^1,2, Anthony Nguyen³, Shinyi Wu³

¹Department of Surgery, University of Southern California, LAC+USC Medical Center (The work was done at LAC+USC Medical Center), Los Angeles, CA, USA.

World journal of surgery

|June 30, 2023

概括

机器学习模型准确地预测了大量的输血需求,超过了现有的得分. 这项技术可以改善创伤环境中的患者护理和资源管理.

科学领域:

医疗信息学医学信息学
创伤外科手术是什么
机器学习应用程序机器学习应用程序

背景情况:

预测大规模输血 (MBT) 需求对于患者的治疗结果和资源分配至关重要.
目前用于识别需要MBT激活的患者的方法可能是不理想的.

研究的目的:

开发和验证机器学习 (ML) 模型,以准确预测MBT的必要性.
将ML模型的性能与已建立的评分系统进行比较.

主要方法:

利用了2015年6月至2019年8月的机构创伤登记数据.
探索各种ML算法,包括后勤回归,SVM,随机森林和神经网络.
使用敏感度,特异性和曲线下的面积 (AUC) 评估模型性能,与ABC和RABT得分进行比较.

主要成果:

总共分析了2438名患者,其中4.9%接受了MBT.
大多数ML模型实现了AUC>0.75,范围为0.75-0.83.
ML模型显示出比ABC和RABT得分更高的灵敏度,同时保持了可比的特异性.

结论:

机器学习模型在预测MBT需求方面显著优于现有的得分.
将ML模型集成到电子健康记录或移动设备中可以提高临床可用性和决策.

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