A Machine-Learned Comorbidity Index

Computer Science > Artificial Intelligence [Submitted on 16 Jun 2026] A Machine-Learned Comorbidity Index Suleman Baloch, Kishlay Jha, Alberto M. Segre, Philip M. Polgreen, Bijaya Adhikari 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. Comments: Accepted at the 43rd International Conference on Machine Learning (ICML 2026), Seoul, South Korea. 35 pages Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.17450 [cs.AI]   (or arXiv:2606.17450v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.17450 Focus to learn more Submission history From: Suleman Baloch [view email] [v1] Tue, 16 Jun 2026 03:07:20 UTC (10,412 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)