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推进多创伤护理:开发和验证用于预测早期死亡率的机器学习模型.

Wen He1, Xianghong Fu1, Song Chen2

  • 1Reproductive Medicine Center, Quzhou People's Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, No. 100, Minjiang Avenue, Quzhou, 324000, Zhejiang, China.

Journal of translational medicine
|September 24, 2023
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概括
此摘要是机器生成的。

机器学习模型可以预测多创伤患者的72小时死亡率. 随机森林模型表现出卓越的性能,帮助临床医生识别高风险个体及时进行干预.

关键词:
死亡率 死亡率 死亡率神经网络的神经网络的神经网络多重创伤的人随机的森林随机的森林在XGBoost中使用.

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科学领域:

  • 医疗信息学 医疗信息学
  • 创伤外科 手术 创伤外科
  • 医疗保健中的机器学习

背景情况:

  • 早期识别高风险多创伤患者对于有效干预和改善生存率至关重要.
  • 机器学习 (ML) 为使用临床数据开发预测模型提供了一个有希望的方法.

研究的目的:

  • 开发和验证ML模型,用于预测成人多创伤患者72小时死亡率.
  • 确定影响死亡率预测的关键临床参数.

主要方法:

  • 从Dryad和机构数据库对多创伤患者的回顾性分析.
  • 开发和验证随机森林 (RF),神经网络和XGBoost模型.
  • 为了模型的可解释性,利用了夏普利的添加式解释 (SHAP) 和局部可解释的模型不可知解释 (LIME).

主要成果:

  • 72小时死亡率的关键预测因素包括年龄,BMI,GCS,ISS,pH,基过量和乳酸.
  • 射频模型实现了高性能:AUROC 0.87 (内部) 和0.98 (外部),AUPRC 0.67 (内部) 和0.88 (外部),精度为0.83 (内部) 和0.97 (外部).
  • 与其他模型相比,射频模型显示出更高的预测效率和临床实用性.

结论:

  • 随机森林模型在预测成人多创伤患者72小时死亡率方面非常有效.
  • 这种ML模型可以帮助临床医生识别有风险的患者,从而指导临床决策并改善患者的治疗结果.