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尿道炎患者的显著不良预后事件:基于机器学习的模型开发和验证研究

Yiqu Wei1,2, Wanqing Xu3, Shuo Yang4

  • 1Department of Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China.

Frontiers in cellular and infection microbiology
|August 27, 2025
PubMed
概括

机器学习模型显示出严重性败血症的预后能力. 使用9个关键变量,XGBoost模型实现了高准确度和AUC,有助于个性化治疗策略.

关键词:
MIMIC-IV 数据库美国机器学习预测模型发生尿

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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