  Clinical databases typically include, for each patient, many heterogeneous
features, for example blood exams, the clinical history before the onset of the
disease, the evolution of the symptoms, the results of imaging exams, and many
others. We here propose to exploit a recently developed statistical approach,
the Information Imbalance, to compare different subsets of patient features,
and automatically select the set of features which is maximally informative for
a given clinical purpose, especially in minority classes. We adapt the
Information Imbalance approach to work in a clinical framework, where patient
features are often categorical and are generally available only for a fraction
of the patients. We apply this algorithm to a data set of ~ 1,300 patients
treated for COVID-19 in Udine hospital before October 2021. Using this
approach, we find combinations of features which, if used in combination, are
maximally informative of the clinical fate and of the severity of the disease.
The optimal number of features, which is determined automatically, turns out to
be between 10 and 15. These features can be measured at admission. The approach
can be used also if the features are available only for a fraction of the
patients, does not require imputation and, importantly, is able to
automatically select features with small inter-feature correlation. Clinical
insights deriving from this study are also discussed.
