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使用多模式电子健康记录数据开发和验证长期COVID风险预测模型使用超级学习算法

Weijia Jin1,2, Wei Hao1,2, Xu Shi1

  • 1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.

Journal of clinical medicine
|December 9, 2023
PubMed
概括

预测COVID-19 (PASC) 后急性后果非常重要. 超重/肥胖,先前使用他类药物和感染期间呼吸道症状是关键预测因素. 综合风险评分有助于识别患PASC风险较高的个人.

关键词:
在 COVID-19 疫情中,电子健康记录是电子健康记录.现型风险评分 现型风险评分在SARS-CoV-2感染的急性后续症状.预测模型的预测模型.

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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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