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Yaorong Ge

University of North Carolina at Charlotte - Department of Software and Information Systems

9201 University City Blvd

Charlotte, NC 28223

United States

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An Interpretable Machine Learning Framework for Accurate Severe vs Non-Severe COVID-19 Clinical Type Classification

Number of pages: 24 Posted: 02 Oct 2020
Yuanfang Chen, Liu Ouyang, Forrest Sheng Bao, Qian Li, Lei Han, Baoli Zhu, Yaorong Ge, Patrick Robinson, Ming Xu, Jie Liu and Shi Chen
Public Health Research Institute of Jiangsu Province, Huazhong University of Science and Technology - Department of Orthopedics, Iowa State University - Department of Computer Science, Affiliated Kunshan Hospital of Jiangsu University - Department of Pediatrics, Jiangsu Provincial Center for Disease Control and Prevention - Institute of HIV/AIDS/STI Prevention and Control, Jiangsu Provincial Center for Disease Control and Prevention - Institute of HIV/AIDS/STI Prevention and Control, University of North Carolina at Charlotte - Department of Software and Information Systems, The University of North Carolina at Charlotte - Department of Public Health Sciences, Jiangsu Provincial Center for Disease Control and Prevention - Institute of HIV/AIDS/STI Prevention and Control, Huazhong University of Science and Technology - Department of Radiology and The University of North Carolina at Charlotte - Department of Public Health Sciences
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COVID-19, machine learning, supervised learning, clinical classification