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Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity

Xiangao Jiang1, Megan Coffee2, 3, *, Anasse Bari4, *, Junzhang Wang4, Xinyue Jiang5, Jianping Huang1, Jichan Shi1, Jianyi Dai1, Jing Cai1, Tianxiao Zhang6, Zhengxing Wu1, Guiqing He1, Yitong Huang7

1 Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, 325000, China.
2 Division of Infectious Diseases and Immunology, Department of Medicine, New York University, New York, USA.
3 Department of Population and Family Health, Mailman School of Public Health, Columbia University, New York, USA.
4 Courant Institute of Mathematical Sciences, Computer Science Department, New York University, New York, USA.
5 Columbia College, Columbia University, New York, USA.
6 Departments of Infectious Diseases, Cangnan People’s Hospital, Wenzhou, 325800, China.
7 Department of Gynaecology, Wenzhou Central Hospital, Wenzhou, 325000, China.

* Corresponding Authors: Anasse Bari, Email: ;
   Megan Coffee, Email: .

Computers, Materials & Continua 2020, 63(1), 537-551.


The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations in a surging pandemic require difficult resource allocation decisions. The objectives of this research are: (1) to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes, and (2) to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation. The predictive models learn from historical data to help predict who will develop acute respiratory distress syndrome (ARDS), a severe outcome in COVID-19. Our results, based on data from two hospitals in Wenzhou, Zhejiang, China, identified features on initial presentation with COVID-19 that were most predictive of later development of ARDS. A mildly elevated alanine aminotransferase (ALT) (a liver enzyme), the presence of myalgias (body aches), and an elevated hemoglobin (red blood cells), in this order, are the clinical features, on presentation, that are the most predictive. The predictive models that learned from historical data of patients from these two hospitals achieved 70% to 80% accuracy in predicting severe cases.


Cite This Article

X. Jiang, M. Coffee, A. Bari, J. Wang, X. Jiang et al., "Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity," Computers, Materials & Continua, vol. 63, no.1, pp. 537–551, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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