Selected article for: "interquartile range and IQR interquartile range"
Author: Xueyan Mei; Hao-Chih Lee; Kaiyue Diao; Mingqian Huang; Bin Lin; Chenyu Liu; Zongyu Xie; Yixuan Ma; Philip M. Robson; Michael Chung; Adam Bernheim; Venkatesh Mani; Claudia Calcagno; Kunwei Li; Shaolin Li; Hong Shan; Jian Lv; Tongtong Zhao; Junli Xia; Qihua Long; Sharon Steinberger; Adam Jacobi; Timothy Deyer; Marta Luksza; Fang Liu; Brent P. Little; Zahi A. Fayad; Yang Yang
Title: Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19)
  • Document date: 2020_4_17
    • ID: 79tozwzq_82
    Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.12.20062661 doi: medRxiv preprint Tables Table 1 . Characteristics of Patient's Clinical Information. The p-value of each clinical feature was tested by logistic regression. The Hosmer-Lemeshow goodness of fit was used to assess the logistic regression fit. Patient's age, presence of exposure to SARS-CoV-2, presence of fever, cough and .....
    Document: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.12.20062661 doi: medRxiv preprint Tables Table 1 . Characteristics of Patient's Clinical Information. The p-value of each clinical feature was tested by logistic regression. The Hosmer-Lemeshow goodness of fit was used to assess the logistic regression fit. Patient's age, presence of exposure to SARS-CoV-2, presence of fever, cough and cough with sputum and white blood cell counts were significant features associated with SARS-CoV-2 status. The logistic regression was a good fit (p=0.66). † Data in parenthesis shows Interquartile Range (IQR)

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