@misc{10481/70598,
year = {2021},
month = {7},
url = {http://hdl.handle.net/10481/70598},
abstract = {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.},
organization = {Ministerio de Ciencia e Innovacion (Espana)/ FEDER	RTI2018-098913-B100},
organization = {Junta de Andalucia},
organization = {European Commission	CV20-45250	
A-TIC-080-UGR18},
publisher = {Nature},
title = {Combining a convolutional neural network with autoencoders to predict the survival chance of COVID‑19 patients},
doi = {10.1038/s41598-021-93543-8},
author = {Khozeimeh, Fahime and Gorriz Sáez, Juan Manuel},
}