ArticleScientific reports2024
Maximally informative feature selection using Information Imbalance: Application to COVID-19 severity prediction.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Temporal rich-club phenomenon and its formation mechanisms in international trade: Evidence from new energy minerals.iScience · 2026Article
- Quantitative Texture Analysis of Cervical Cytology Identifies Endometrial Lesions in Atypical Glandular Cells on Liquid-Based Cytology: A Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
- Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance.Nature communications · 2025Article
- Investigating the relationship between the immune response and the severity of COVID-19: a large-cohort retrospective study.Frontiers in immunology · 2024Observational
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Authors and funding
9 authors.
Funding
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 that 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
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