Author: Jiangpeng Wu; Pengyi Zhang; Liting Zhang; Wenbo Meng; Junfeng Li; Chongxiang Tong; Yonghong Li; Jing Cai; Zengwei Yang; Jinhong Zhu; Meie Zhao; Huirong Huang; Xiaodong Xie; Shuyan Li
Title: Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results Document date: 2020_4_6
ID: kjovtgua_41
Snippet: The copyright holder for this preprint . https://doi.org/10.1101/2020.04.02.20051136 doi: medRxiv preprint balance was often broken and the composition was changed in the blood. The timely responses might be used for the identification of the disease as long as they could be captured by machine learning algorithms. And the assistant tool was not subject to complex operation and prolonged waiting because the conventional laboratory blood tests wer.....
Document: The copyright holder for this preprint . https://doi.org/10.1101/2020.04.02.20051136 doi: medRxiv preprint balance was often broken and the composition was changed in the blood. The timely responses might be used for the identification of the disease as long as they could be captured by machine learning algorithms. And the assistant tool was not subject to complex operation and prolonged waiting because the conventional laboratory blood tests were the mature technology that could be fully automated. It was convenient to operate the large-scale initial screening and help the confirmed patients provide appropriate treatment.
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