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With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is ofgreat importance to conduct early diagnosis of COVID-19 and predict the timethat patients might convert to the severe stage, for designing effectivetreatment plan and reducing the clinicians' workloads. In this study, wepropose a joint classification and regression method to determine whether thepatient would develop severe symptoms in the later time, and if yes, predictthe possible conversion time that the patient would spend to convert to thesevere stage. To do this, the proposed method takes into account 1) the weightfor each sample to reduce the outliers' influence and explore the problem ofimbalance classification, and 2) the weight for each feature via a sparsityregularization term to remove the redundant features of high-dimensional dataand learn the shared information across the classification task and theregression task. To our knowledge, this study is the first work to predict thedisease progression and the conversion time, which could help clinicians todeal with the potential severe cases in time or even save the patients' lives.Experimental analysis was conducted on a real data set from two hospitals with422 chest computed tomography (CT) scans, where 52 cases were converted tosevere on average 5.64 days and 34 cases were severe at admission. Results showthat our method achieves the best classification (e.g., 85.91% of accuracy) andregression (e.g., 0.462 of the correlation coefficient) performance, comparedto all comparison methods. Moreover, our proposed method yields 76.97% ofaccuracy for predicting the severe cases, 0.524 of the correlation coefficient,and 0.55 days difference for the converted time.

Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan