  Recent epidemiological data report that worldwide more than 53 million people
have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease
has been spreading very rapidly and few months after the identification of the
first infected, shortage of hospital resources quickly became a problem. In
this work we investigate whether chest X-ray (CXR) can be used as a possible
tool for the early identification of patients at risk of severe outcome, like
intensive care or death. CXR is a radiological technique that compared to
computed tomography (CT) it is simpler, faster, more widespread and it induces
lower radiation dose. We present a dataset including data collected from 820
patients by six Italian hospitals in spring 2020 during the first COVID-19
emergency. The dataset includes CXR images, several clinical attributes and
clinical outcomes. We investigate the potential of artificial intelligence to
predict the prognosis of such patients, distinguishing between severe and mild
cases, thus offering a baseline reference for other researchers and
practitioners. To this goal, we present three approaches that use features
extracted from CXR images, either handcrafted or automatically by convolutional
neuronal networks, which are then integrated with the clinical data. Exhaustive
evaluation shows promising performance both in 10-fold and leave-one-centre-out
cross-validation, implying that clinical data and images have the potential to
provide useful information for the management of patients and hospital
resources.
