Researchers developed CFGNN (Causal Factor-aware Graph Neural Network), a machine learning system that creates personalized cardiovascular risk networks for each myocardial infarction patient. The model constructs differential networks capturing individual-specific deviations in how cardiovascular factors interact, then uses these networks to predict recurrence risk while identifying key causal risk factors. Testing on real-world hospital datasets showed the system effectively pinpointed several critical risk factors through its network-based approach. This represents a promising shift toward personalized cardiovascular risk assessment, moving beyond traditional one-size-fits-all models to capture the complex, patient-specific interplay of cardiovascular factors. The causal perspective could help clinicians understand not just correlations but actual cause-and-effect relationships driving recurrence risk. However, as an unpeer-reviewed preprint, these results require validation through rigorous review and independent replication. The study's impact depends heavily on the size and diversity of the hospital datasets used, which aren't specified in the abstract. While the personalized network approach appears innovative, its clinical utility will ultimately depend on whether it meaningfully improves prediction accuracy over existing risk calculators and whether the identified causal factors translate into actionable interventions for preventing subsequent heart attacks.