  COVID-19 has caused many deaths worldwide. The automation of the diagnosis of
this virus is highly desired. Convolutional neural networks (CNNs) have shown
outstanding classification performance on image datasets. To date, it appears
that COVID computer-aided diagnosis systems based on CNNs and clinical
information have not yet been analysed or explored. We propose a novel method,
named the CNN-AE, to predict the survival chance of COVID-19 patients using a
CNN trained with clinical information. Notably, the required resources to
prepare CT images are expensive and limited compared to those required to
collect clinical data, such as blood pressure, liver disease, etc. We evaluated
our method using a publicly available clinical dataset that we collected. The
dataset properties were carefully analysed to extract important features and
compute the correlations of features. A data augmentation procedure based on
autoencoders (AEs) was proposed to balance the dataset. The experimental
results revealed that the average accuracy of the CNN-AE (96.05%) was higher
than that of the CNN (92.49%). To demonstrate the generality of our
augmentation method, we trained some existing mortality risk prediction methods
on our dataset (with and without data augmentation) and compared their
performances. We also evaluated our method using another dataset for further
generality verification. To show that clinical data can be used for COVID-19
survival chance prediction, the CNN-AE was compared with multiple pre-trained
deep models that were tuned based on CT images.
