A Machine-Learned Comorbidity Index

Suleman Baloch; Kishlay Jha; Alberto M Segre; Philip M Polgreen; Bijaya Adhikari

doi:10.48550/arxiv.2606.17450

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A Machine-Learned Comorbidity Index

Suleman Baloch, Kishlay Jha, Alberto M Segre, Philip M Polgreen and Bijaya Adhikari

ArXiv.org

Cornell University

06/16/2026

DOI: 10.48550/arxiv.2606.17450

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Preprint (Author's original) This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Traditional comorbidity scores (e.g., Charlson and Elixhauser) are widely used for risk adjustment and patient stratification, but they have two key limitations: (i) they are largely mortality-centric and do not align well with other clinical outcomes, and (ii) their linear, rule-based structure cannot capture nonlinear, outcome-specific risk relationships. We propose a Machine-Learned Comorbidity Index (MLCI) that maps diagnosis codes to a single scalar by maximizing the normalized Hilbert-Schmidt Independence Criterion (nHSIC) between the learned score and multiple clinical outcomes. MLCI captures nonlinear risk-outcome dependence and is supported by a theory that characterizes when a unified, informative admission-level ordering can be achieved across outcomes. Empirical results on multiple benchmark electronic health record (EHR) datasets show that MLCI outperforms strong baselines across multiple evaluation metrics.

Computer Science - Artificial Intelligence

Details

Title: Subtitle: A Machine-Learned Comorbidity Index
Creators: Suleman Baloch - University of Iowa
Kishlay Jha - University of Iowa, Electrical and Computer Engineering
Alberto M Segre - University of Iowa
Philip M Polgreen - University of Iowa
Bijaya Adhikari - University of Iowa
Resource Type: Preprint
Publication Details: ArXiv.org
DOI: 10.48550/arxiv.2606.17450
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 06/16/2026
Academic Unit: Statistics and Actuarial Science; Electrical and Computer Engineering; Infectious Diseases; Epidemiology; Nursing; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Computer Science; Internal Medicine
Record Identifier: 9985175374302771

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