arXiv 2605.072672026-07-08cs.LG

PerCaM-Health：面向医疗推理的个性化动态因果图

PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning

University of California, Irvine（加州大学尔湾分校）；Washington University in St. Louis（圣路易斯华盛顿大学）

Elahe Khatibi, Ziyu Wang, Saba A. Farahani, Di Huang, Hung Cao, Ramesh Jain, Amir M. Rahmani

备注：: This paper has been withdrawn by the authors to allow further internal review of privacy, permission, and attribution considerations before public dissemination

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AI总结：

本文提出PerCaM-Health框架，通过学习纵向健康数据中的个性化动态因果图，提升医疗干预推理的可靠性与可解释性。

摘要：

个性化医疗决策需要推理生理和行为变量如何随时间影响个体患者。现有时间因果发现方法难以满足这一需求：群体级模型提供稳定但非个性化的结构，而单患者发现不可靠，因为个体轨迹短、噪声大、不规则且非平稳。本文引入PerCaM-Health框架，从纵向健康数据中学习个性化动态因果图。该框架首先学习知识引导的群体时间图，然后保守地适应和进化该图，利用患者特定的时间证据和滑动窗口更新，生成可解释且可审计的图序列。通过将这些图与时间结构方程结合，该框架使患者层面的反事实查询成为可能，例如估计在假设行为干预下的短期结果变化。实验表明，PerCaM-Health在图恢复、动态边跟踪和干预方向准确性方面优于群体级、单患者和非个性化时间基线。这些结果表明，联合建模个性化和时间演进能产生更可靠的因果结构和干预推理。

英文摘要：

Personalized healthcare decisions require reasoning about how physiological and behavioral variables influence an individual patient over time. Existing temporal causal discovery methods are poorly matched to this setting: cohort-level models provide stable but non-personalized structures, while per-patient discovery is unreliable because individual trajectories are short, noisy, irregular, and non-stationary. This creates a fundamental gap between population-level causal modeling and the patient-specific, time-varying mechanisms needed for intervention reasoning. We introduce PerCaM-Health, a framework for learning personalized dynamic causal graphs from longitudinal health data. The framework learns a knowledge-guided population temporal graph, then conservatively adapts and evolves it using patient-specific temporal evidence and rolling-window updates, producing interpretable and auditable graph sequences. By coupling these graphs with temporal structural equations, the framework enables patient-level counterfactual queries, such as estimating short-horizon outcome changes under hypothetical behavioral interventions. Experiments on a semi-synthetic dynamic health benchmark show that PerCaM-Health improves graph recovery, dynamic edge tracking, and intervention direction accuracy compared to cohort-level, per-patient, and non-personalized temporal baselines. These results demonstrate that jointly modeling personalization and temporal evolution yields more reliable causal structure and intervention reasoning.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。