  With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of
great importance to conduct early diagnosis of COVID-19 and predict the time
that patients might convert to the severe stage, for designing effective
treatment plan and reducing the clinicians' workloads. In this study, we
propose a joint classification and regression method to determine whether the
patient would develop severe symptoms in the later time, and if yes, predict
the possible conversion time that the patient would spend to convert to the
severe stage. To do this, the proposed method takes into account 1) the weight
for each sample to reduce the outliers' influence and explore the problem of
imbalance classification, and 2) the weight for each feature via a sparsity
regularization term to remove the redundant features of high-dimensional data
and learn the shared information across the classification task and the
regression task. To our knowledge, this study is the first work to predict the
disease progression and the conversion time, which could help clinicians to
deal 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 with
422 chest computed tomography (CT) scans, where 52 cases were converted to
severe on average 5.64 days and 34 cases were severe at admission. Results show
that our method achieves the best classification (e.g., 85.91% of accuracy) and
regression (e.g., 0.462 of the correlation coefficient) performance, compared
to all comparison methods. Moreover, our proposed method yields 76.97% of
accuracy for predicting the severe cases, 0.524 of the correlation coefficient,
and 0.55 days difference for the converted time.
