Maximally informative feature selection using Information Imbalance: Application to COVID-19 severity prediction

TL;DR

This paper introduces a method using Information Imbalance to select the most informative clinical features for COVID-19 severity prediction, effectively handling heterogeneous, categorical, and incomplete data.

Contribution

It adapts the Information Imbalance technique for clinical data, enabling automatic, robust feature selection that accounts for feature correlation and data incompleteness.

Findings

Selected features are measurable at admission.

Optimal feature set size is 10-15 features.

Method outperforms traditional feature selection approaches.

Abstract

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.