  The application of computer vision for COVID-19 diagnosis is complex and
challenging, given the risks associated with patient misclassifications.
Arguably, the primary value of medical imaging for COVID-19 lies rather on
patient prognosis. Radiological images can guide physicians assessing the
severity of the disease, and a series of images from the same patient at
different stages can help to gauge disease progression. Hence, a simple method
based on lung-pathology interpretable features for scoring disease severity
from Chest X-rays is proposed here. As the primary contribution, this method
correlates well to patient severity in different stages of disease progression
with competitive results compared to other existing, more complex methods. An
original data selection approach is also proposed, allowing the simple model to
learn the severity-related features. It is hypothesized that the resulting
competitive performance presented here is related to the method being
feature-based rather than reliant on lung involvement or opacity as others in
the literature. A second contribution comes from the validation of the results,
conceptualized as the scoring of patients groups from different stages of the
disease. Besides performing such validation on an independent data set, the
results were also compared with other proposed scoring methods in the
literature. The results show that there is a significant correlation between
the scoring system (MAVIDH) and patient outcome, which could potentially help
physicians rating and following disease progression in COVID-19 patients.
