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机器学习算法可以帮助我们预测医院前的大量出血吗?

Marcos Valiente Fernández1, Carlos García Fuentes1, Francisco de Paula Delgado Moya1

  • 1Hospital Universitario 12 de Octubre, UCI de Trauma y Emergencias, Madrid. Spain.

Medicina intensiva
|July 28, 2023
PubMed
概括

机器学习算法 (MLA) 在预测严重创伤性伤害 (STI) 患者大规模出血 (MH) 方面明显优于传统预测量表 (TPS). MLA实现了高预测准确度,为医院外紧急护理提供了有价值的工具.

关键词:
临床分数 临床分数巨大的出血.机器学习 机器学习巨大的出血.在医院外的治疗.医院前医院 (prehospitalaria) 是一个医院.临床分数 临床分数创伤是一个创伤.

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科学领域:

  • 紧急医疗 紧急医疗
  • 数据科学数据科学数据科学
  • 创伤外科 手术 创伤外科

背景情况:

  • 大规模出血 (MH) 是严重创伤性伤害 (STI) 中的一个关键问题.
  • 准确预测MH对于及时和有效的干预至关重要.
  • 传统的预测尺度 (TPS) 在预测STI中的MH方面存在局限性.

研究的目的:

  • 为了比较机器学习算法 (MLA) 与在性病患者中对MH的TPS的预测性能.
  • 评估MLA在外科医院创伤护理环境中的实用性.

主要方法:

  • 对473名性传播疾病患者进行了回顾性分析,并提供了医院前数据.
  • 使用80%的培训和20%的验证数据开发和验证四个MLA (随机森林,SVM,GBM,NN).
  • 使用接收器操作特征 (ROC) 曲线和Shapley值的变量重要性来评估预测功率.

主要成果:

  • MLAs实现了高预测准确度,ROC值超过0.85,中位数接近0.98.
  • 测试的MLA的表现之间没有发现显著差异.
  • 由MLAs确定的关键预测变量包括血液动力学状态,复苏因子和神经功能障碍.

结论:

  • 机器学习算法在严重的创伤损伤中显示出高超的大量出血预测能力,相比于传统的严重创伤伤害预测能力.
  • 法律法官为改善医院前护理和创伤患者的治疗结果提供了有希望的进步.