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This research introduces the Machine-Learned Comorbidity Index (MLCI), which leverages advanced machine learning techniques to overcome the linear limitations of traditional clinical scoring systems like Charlson and Elixhauser. By utilizing a normalized Hilbert-Schmidt Independence Criterion, MLCI effectively captures nonlinear relationships between diagnosis codes and diverse patient outcomes across multiple electronic health record datasets. The method demonstrates superior performance in risk adjustment and patient stratification compared to existing baselines, marking a significant advancement in clinical AI applications.
A Machine-Learned Comorbidity Index
arXiv:2606.17450v2 Announce Type: replace 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.
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.
Accepted at the 43rd International Conference on Machine Learning (ICML 2026), Seoul, South Korea. 35 pages
Artificial Intelligence (cs.AI)
arXiv:2606.17450 [cs.AI]
(or arXiv:2606.17450v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.17450 Focus to learn more arXiv-issued DOI via DataCite
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