发表机构
University of Science and Technology of China; Sun Yat-sen University（中国科学技术大学; 中山大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对纵向电子健康记录生成中标准模型的不足，提出AdaPCLA框架，通过数据分布感知训练策略、模拟退火训练及零样本分布控制实现自适应拟合与生成，理论分析刻画相关界限，实验验证其在多方面有提升。
AI 中文摘要
纵向电子健康记录的生成建模对隐私保护研究愈发重要，但标准自回归模型往往无法充分体现尾部事件的共现结构，降低了为罕见亚群体生成数据的保真度。为此，我们提出AdaPCLA框架，通过数据分布感知训练策略让生成模型自适应拟合和生成电子健康记录数据，通过模拟退火训练内化数据知识参数，还支持通过零样本分布控制对不同临床群体进行无训练适应。理论分析通过标签级经验NTK刻画罕见代码逻辑更新，并得出退火速度和NTK条件对保留先验信号影响的先验内化界限。实验表明AdaPCLA在尾部合理性、下游效用和零样本控制方面均有提升。
英文摘要
Generative modeling of longitudinal Electronic Health Records is increasingly important for privacy-preserving research, yet standard autoregressive models tend to underrepresent the co-occurrence structure of tail events (i.e., diseases, symptoms), reducing the fidelity and faithfulness of generated data for rare subpopulations. To this end, we propose AdaPCLA framework, which enables generative models to adaptively fit and generate EHR data through a data distribution-aware training strategy; this is achieved by internalizing data knowledge parameters by simulated annealing training. It also supports training-free adaptation to a diverse clinical population for generation through zero-shot distribution control. Moreover, our theoretical analysis characterizes rare-code logit updates through the label-wise empirical NTK and derives a prior-internalization bound for how annealing speed and NTK conditioning affect retained prior signals. Experiments on real-world data show that AdaPCLA achieves consistent gains in tail plausibility, downstream utility, and zero-shot control; in particular, it improves TailPairSeen over HALO by 114.2% on MIMIC-III and 65.1% on MIMIC-IV, outperforms GPT-style generation by 3.5% F1 for zero-shot cross-population adaptation.
Comments40 pages, 10 figures
