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_77
Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the .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 The two-sided 95% confidence interval of sensitivity and specificity was calculated by the exact method 33 . The confidence interval of AUC was calculated by the DeLong methods 34 . McNemar's test 35 was used to compare the performance between models and human readers. The Youden index was used to determ.....
Document: The copyright holder for this preprint (which was not peer-reviewed) is the .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 The two-sided 95% confidence interval of sensitivity and specificity was calculated by the exact method 33 . The confidence interval of AUC was calculated by the DeLong methods 34 . McNemar's test 35 was used to compare the performance between models and human readers. The Youden index was used to determine the optimal model sensitivity and specificity. Statistical significance was defined as a p-value less than 0.05. Logistic regression was used to evaluate the significance of each clinical variable. Hosmer-Lemeshow goodness of fit 36 was used to assess the goodness of fit of the logistic regression. The statistics of AUC comparisons were computed in the pROC package 37 . Other statistics were computed in python 3.6.5.
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