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Updated: Jan 9, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
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使用机器学习和深度学习预测败血症死亡率 - - 一项系统性审查.

Mohannad N AbuHaweeleh1,2, Adiba Tabassum Chowdhury3, Mehrin Newaz3

  • 1Department of Basic Medical Sciences, College of Medicine, Qatar University, Doha, 2713, Qatar.

BMC medical informatics and decision making
|December 10, 2025
PubMed
概括

机器学习和深度学习显示出使用电子健康记录 (EHR) 预测败血症的前景. 为了可靠的早期检测和改善患者的治疗结果,需要标准化的方法.

关键词:
临床决策的过程深度学习是一种深度学习.功能提取 功能提取机器学习 机器学习模型的解释性 模型的解释性预测分析是一种预测分析.实时监控实时监控败血症死亡率预测预测

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能在医学中的应用
  • 计算生物学 计算生物学

背景情况:

  • 败血症是一种危及生命的炎症状况,需要及时诊断.
  • 电子健康记录 (EHR) 为败血症预测提供了大量数据.
  • 机器学习 (ML) 和深度学习 (DL) 是医疗保健的新兴工具.

研究的目的:

  • 利用EHR数据系统地审查ML/DL在败血症预测中的应用.
  • 确定这一领域的趋势,挑战和未来方向.

主要方法:

  • 在主要的科学数据库 (PubMed,IEEE Xplore,Google Scholar,Scopus) 中进行全面的文献搜索.
  • 纳入标准适用于39项选定的研究.
  • 对研究特征,方法和报告结果的分析.

主要成果:

  • 大多数研究 (34/39) 是回顾性和地理多样性的.
  • 在数据集,败血症定义,模型参数和质量评估中观察到显著的异质性.
  • 纵向EHR数据显示了早期败血症预测的潜力,尽管存在差异.

结论:

  • ML/DL 方法对使用 EHR 进行早期的败血症检测和预测具有重大前景.
  • 标准化的评估指标和质量评估对于推进该领域至关重要.
  • 解决数据异质性和资金差异对于可靠的实施至关重要.