aiqbench analysis
This research introduces a machine-learned comorbidity index (MLCI) that overcomes the linear limitations of traditional risk scores by capturing nonlinear relationships between diagnosis codes and diverse clinical outcomes using nHSIC. Accepted at ICML 2026, the method demonstrates superior performance on benchmark EHR datasets for patient stratification beyond simple mortality prediction. The work represents a significant step toward more robust AI-driven tools in healthcare analytics that align with specific treatment goals rather than just survival rates.
A Machine-Learned Comorbidity Index
arXiv:2606.17450v1 Announce Type: new 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:2606.17450v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.17450 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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