用机器学习构建新共病指数，更好预测多种临床结果。
A Machine-Learned Comorbidity Index
- 基于nHSIC优化，学习诊断码到风险分的非线性映射。
- 在多个真实EHR数据集上，对多种临床结局预测均优于传统方法。
- 适合关注多目标风险评估的临床研究者和医疗算法开发者。
传统共病评分（如Charlson和Elixhauser）广泛用于风险调整和患者分层，但存在两大局限：(i) 主要以死亡率为中心，难以匹配其他临床结局；(ii) 线性规则结构无法捕捉非线性、结果特异的风险关系。本文提出机器学习共病指数（MLCI），通过最大化学习得分与多种临床结局之间的归一化希尔伯特-施密特独立性准则（nHSIC），将诊断码映射为单一标量。MLCI能捕捉非线性风险-结局依赖关系，并有理论支撑统一的入院级排序在多结局下仍具信息量。在多个基准电子健康记录（EHR）数据集上的实证结果显示，MLCI在多项评估指标上均显著优于强基线模型。
原文摘要 · Abstract (English)
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.
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