Selected article for: "acute respiratory syndrome and machine capacity"

Author: Zhang, Linlin; Wang, Diane C.; Huang, Qihong; Wang, Xiangdong
Title: Significance of clinical phenomes of patients with COVID‐19 infection: A learning from 3795 patients in 80 reports
  • Cord-id: 3dejrzqi
  • Document date: 2020_4_4
  • ID: 3dejrzqi
    Snippet: A new coronavirus SARS‐CoV‐2 has caused outbreaks in multiple countries and the number of cases is rapidly increasing through human‐to‐human transmission. Clinical phenomes of patients with SARS‐CoV‐2 infection are critical in distinguishing it from other respiratory infections. The extent and characteristics of those phenomes varied depending on the severities of the infection, for example, beginning with fever or a mild cough, progressed with signs of pneumonia, and worsened with s
    Document: A new coronavirus SARS‐CoV‐2 has caused outbreaks in multiple countries and the number of cases is rapidly increasing through human‐to‐human transmission. Clinical phenomes of patients with SARS‐CoV‐2 infection are critical in distinguishing it from other respiratory infections. The extent and characteristics of those phenomes varied depending on the severities of the infection, for example, beginning with fever or a mild cough, progressed with signs of pneumonia, and worsened with severe or even fatal respiratory difficulty in acute respiratory distress syndrome. We summarized clinical phenomes of 3795 patients with COVID‐19 based on 80 published reports from the onset of outbreak to March 2020 to emphasize the importance and specificity of those phenomes in diagnosis and treatment of infection, and evaluate the impact on medical services. The data show that the incidence of male patients was higher than that of females and the level of C‐reaction protein was increased as well as most patients' imaging included ground‐glass opacity. Clinical phenomes of SARS‐CoV‐2 infection were compared with those of SARS‐CoV and MERS‐CoV infections. There is an urgent need to develop an artificial intelligence‐based machine learning capacity to analyze and integrate radiomics‐ or imaging‐based, patient‐based, clinician‐based, and molecular measurements‐based data to fight the outbreak of COVID‐19 and enable more efficient responses to unknown infections in future.

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