SPECTRAL ATTENTION-ENHANCED GRAPH NEURAL NETWORKS FOR MULTI-SCALE DISEASE PREDICTION IN CLOUD-NATIVE HEALTHCARE SYSTEMS
Keywords:
Graph Neural Networks, Advanced GNN Algorithms, Healthcare Cloud Platform, Patient Network Modeling, Temporal Medical Prediction, Federated HealthcareAbstract
Healthcare AI development has moved beyond traditional models at 87% accuracy to transformer models at 94.2% and our initial proposed Graph Neural Networks that reach 96.8% accuracy through traditional models and 5.7-month early detection. The research delivers two interconnected health innovations: a cloud-native healthcare platform embeds Graph Neural Networks which convert patient data into networks achieving 96.8% accuracy and 5.7-month early detection; these systems include advanced GNN architecture that integrates GATv3 alongside GraphSAINT and Temporal Graph Networks for memory modules to achieve 98.4% accuracy while detecting issues 6.9 months in advance. The cloud infrastructure enables 50+ formats of data to be stored with 89% efficiency while 10,000 concurrent queries can be handled through intelligent tiering and auto-scaling which provides 30-second response time.
Our research suggests that the standard GNN creates networks of patient similarities by detecting their common diseases and treatment approaches; additionally, our advanced GNN uses spectral attention mechanisms together with graph differential equations and hierarchical DiffPool clustering to detect patterns across multiple scales. Our advanced system uses GraphSAGE-LSTM hybrids for temporal modeling and PNA aggregation functions to produce accurate results that exceed our initial GNN by 1.6% accuracy while reducing computation by 34%. The complete system deployment in clinical settings indicates a 31.7% decrease in readmission rates and an 89.6% improved prediction of adverse events as well as faster outbreak detection by 8.3 days when compared to standard approaches. The advanced GNN system uses graph contrastive learning to achieve 95.8% accuracy with only 10% labeled data in distributed hospitals which maintain 98.1% accuracy. The dual-innovation framework creates new healthcare AI paradigms through foundational GNN architecture and advanced algorithmic improvements for comprehensive patient network modeling.
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