  X-ray images may present non-trivial features with predictive information of
patients that develop severe symptoms of COVID-19. If true, this hypothesis may
have practical value in allocating resources to particular patients while using
a relatively inexpensive imaging technique. The difficulty of testing such a
hypothesis comes from the need for large sets of labelled data, which need to
be well-annotated and should contemplate the post-imaging severity outcome.
This paper presents an original methodology for extracting semantic features
that correlate to severity from a data set with patient ICU admission labels
through interpretable models. The methodology employs a neural network trained
to recognise lung pathologies to extract the semantic features, which are then
analysed with low-complexity models to limit overfitting while increasing
interpretability. This analysis points out that only a few features explain
most of the variance between patients that developed severe symptoms. When
applied to an unrelated larger data set with pathology-related clinical notes,
the method has shown to be capable of selecting images for the learned
features, which could translate some information about their common locations
in the lung. Besides attesting separability on patients that eventually develop
severe symptoms, the proposed methods represent a statistical approach
highlighting the importance of features related to ICU admission that may have
been only qualitatively reported. While handling limited data sets, notable
methodological aspects are adopted, such as presenting a state-of-the-art lung
segmentation network and the use of low-complexity models to avoid overfitting.
The code for methodology and experiments is also available.
