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基于机器学习的Clostridioides difficile感染预测模型:系统审查

Raseen Tariq1, Sheza Malik2, Renisha Redij3

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机器学习模型显示了预测Clostridioides difficile感染 (CDI) 发生率和结果的潜力. 然而,CDI定义的变化和有限的外部验证阻碍了广泛的临床使用.

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科学领域:

  • 传染性疾病 传染性疾病
  • 医疗信息学 医疗信息学
  • 计算生物学 计算生物学

背景情况:

  • 预测Clostridioides difficile感染 (CDI) 的发生率和结果是具有挑战性的.
  • 电子健康记录 (EHR) 为预测建模提供了有价值的临床数据.
  • 机器学习 (ML) 为改善CDI预测提供了一个有希望的途径.

研究的目的:

  • 系统地审查和评估ML模型在预测CDI发病率和并发症方面的性能.
  • 评估ML模型的性能,使用来自EHR的临床数据.
  • 确定常见的ML技术及其在CDI预测中的有效性.

主要方法:

  • 在主要数据库中进行全面的文献搜索,直到2023年9月.
  • 包括使用ML进行CDI预测的回顾性研究.
  • 基于接收器运行特征曲线 (AUC) 下面面积的性能评估.

主要成果:

  • 包括12项研究,主要使用随机森林和梯度增强ML模型.
  • 预测CDI发病率的AUC值在0.60至0.81.8之间.
  • 复发和并发症的AUC值分别在0.59-0.80和0.64-0.88之间.
  • 机器学习模型的性能与后勤回归相似,但CDI定义的异质性和缺乏外部验证得到了注意.

结论:

  • 机器学习模型显示了预测CDI发生率和结果的潜力.
  • CDI定义中的异质性和外部验证不足对临床实施构成挑战.
  • 未来的研究应该优先考虑外部验证和标准化的CDI定义.