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Updated: Jul 16, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

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Published on: December 19, 2020

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在COVID-19肺炎的临床严重程度类别具有不同的免疫学特征,通过机器学习促进风险分层

Laura Wiffen1, Leon Gerard D'Cruz1,2, Thomas Brown1

  • 1Research and Innovation Department, Portsmouth Hospitals University National Health Service (NHS) Trust, Portsmouth, United Kingdom.

Frontiers in immunology
|September 21, 2023
PubMed
概括

血清生物标志物可以帮助非专家对2019年冠状病毒病 (COVID-19) 患者进行分拣. 机器学习模型确定了预测COVID-19严重程度和患者恶化的关键生物标志物,有助于临床决策.

关键词:
在 COVID-19 疫情中,生物标志物生物标志物补充补充补充补充补充补充补充.细胞因子细胞因子机器学习是机器学习.在 nomacopanopan 中.风险分层的分层是风险分层.

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