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_39
Snippet: Patient's age, sex, exposure history, symptoms (present or absent of fever, cough and/or sputum), white blood cell counts, absolute neutrophil number, neutrophil percentage, absolute lymphocyte number and lymphocyte percentage were collected (Table 1) . Sex, exposure history and symptoms were categorical variables. We used the LabelEncoder function in scikit-learn package to encode the target categorical variables into numerical variables with va.....
Document: Patient's age, sex, exposure history, symptoms (present or absent of fever, cough and/or sputum), white blood cell counts, absolute neutrophil number, neutrophil percentage, absolute lymphocyte number and lymphocyte percentage were collected (Table 1) . Sex, exposure history and symptoms were categorical variables. We used the LabelEncoder function in scikit-learn package to encode the target categorical variables into numerical variables with value between 0 and 1. Then, we normalized each feature within a range of 0 and 1 using MinMaxScaler function in the scikit-learn package for further model development.
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