  The COVID-19 pandemic has had a considerable impact on day-to-day life.
Tackling the disease by providing the necessary resources to the affected is of
paramount importance. However, estimation of the required resources is not a
trivial task given the number of factors which determine the requirement. This
issue can be addressed by predicting the probability that an infected patient
requires Intensive Care Unit (ICU) support and the importance of each of the
factors that influence it. Moreover, to assist the doctors in determining the
patients at high risk of fatality, the probability of death is also calculated.
For determining both the patient outcomes (ICU admission and death), a novel
methodology is proposed by combining multi-modal features, extracted from
Computed Tomography (CT) scans and Electronic Health Record (EHR) data. Deep
learning models are leveraged to extract quantitative features from CT scans.
These features combined with those directly read from the EHR database are fed
into machine learning models to eventually output the probabilities of patient
outcomes. This work demonstrates both the ability to apply a broad set of deep
learning methods for general quantification of Chest CT scans and the ability
to link these quantitative metrics to patient outcomes. The effectiveness of
the proposed method is shown by testing it on an internally curated dataset,
achieving a mean area under Receiver operating characteristic curve (AUC) of
0.77 on ICU admission prediction and a mean AUC of 0.73 on death prediction
using the best performing classifiers.
