Selected article for: "dataset split and training set"
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_15
    Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the .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 (confirmed by at least two additional negative RT-PCR tests and clinical observation). We randomly split the dataset into 60% training set (534 cases; 242 SARS-CoV-2 (+); 292 SARS-CoV-2 (-) cases), 10% tuning set (92 cases; 43 SARS-CoV-2 (+) cases; 49 SARS-CoV-2 (-) cases), 30% testing set (279 cases; 13.....
    Document: The copyright holder for this preprint (which was not peer-reviewed) is the .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 (confirmed by at least two additional negative RT-PCR tests and clinical observation). We randomly split the dataset into 60% training set (534 cases; 242 SARS-CoV-2 (+); 292 SARS-CoV-2 (-) cases), 10% tuning set (92 cases; 43 SARS-CoV-2 (+) cases; 49 SARS-CoV-2 (-) cases), 30% testing set (279 cases; 134 SARS-CoV-2 (+) cases; 145 SARS-CoV-2 (-) cases) (Extended Fig. 1 ).

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