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开发和验证一种基于机器学习的模型,用于治疗败血症后的脆弱性.

Hye Ju Yeo1,2,3, Dasom Noh4,3, Tae Hwa Kim1

  • 1Division of Allergy, Pulmonary and Critical Care Medicine, Department of Internal Medicine, Transplant Research Center, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea.

ERJ open research
|October 8, 2024
PubMed
概括

预测败血症后的脆弱性是一项挑战. 使用常规临床数据的机器学习模型在识别患有败血症后虚弱风险的患者时取得了高准确性.

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科学领域:

  • 关键护理医学 关键护理医学
  • 老年学是一门学科.
  • 医疗保健中的人工智能

背景情况:

  • 败血症后的脆弱性是一个普遍且重要的临床挑战.
  • 预测败血症后虚弱的发展仍然很困难.

研究的目的:

  • 开发和验证一种机器学习模型,用于预测败血症幸存者的脆弱性.
  • 确定用于预测败血症后脆弱性的关键临床变量.

主要方法:

  • 使用来自韩国国家多中心前性观察队伍 (2019年9月至2021年12月) 的数据开发了一个深度学习模型.
  • 用10个常规收集的败血症变量来训练6个机器学习模型,包括传统和神经网络方法.
  • 用交叉验证和时间验证评估模型性能,包括使用COVID-19数据集进行外部验证.

主要成果:

  • 分析了8518名败血症患者;64.1%的患者在出院时被确定为虚弱.
  • 极端梯度提升 (XGB) 模型表现出最高的性能,AUC为0.8175,准确度为0.7414.
  • 外部验证证实了XGB模型的概括性,达到0.7668.8的AUC.

结论:

  • 一个基于机器学习的模型有效地预测了败血症后的脆弱性.
  • 使用有限的一组基线临床参数实现了高预测性能.