# A Machine-Learned Comorbidity Index

arXiv: https://arxiv.org/abs/2606.17450 · HTML: https://mlsanity.com/paper/2606.17450/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 41 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Self-Supervised Learning with Kernel Dependence Maximization](https://mlsanity.com/paper/2106.08320/) (2021)
- [A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data](https://mlsanity.com/paper/1404.2124/) (2014)

## Abstract (arXiv)

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.