PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning

• URL: http://arxiv.org/abs/2605.07267v1
• Date: Fri, 08 May 2026 05:31:45 GMT
• Title: PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning
• Abstract summary: PerCaM-Health is a framework for learning personalized dynamic causal graphs from longitudinal health data.<n>By coupling these graphs with temporal structural equations, the framework enables patient-level counterfactual queries.
• Score: 26.25923484672118
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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