@Article{cmc.2020.010691,
AUTHOR = {Xiangao Jiang, Megan Coffee, Anasse Bari, Junzhang Wang, Xinyue Jiang, Jianping Huang, Jichan Shi, Jianyi Dai, Jing Cai, Tianxiao Zhang, Zhengxing Wu, Guiqing He, Yitong Huang},
TITLE = {Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {1},
PAGES = {537--551},
URL = {http://www.techscience.com/cmc/v63n1/38464},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2020.010691}
}