GAT-Enhanced TabNet model with heterogeneous tabular and dependency graph information feature fusion for...

Modeling structured medical tabular data presents significant challenges due to complex sample dependencies and non-linear feature interactions. Existing methods, which primarily focus on single-disease prediction, often exhibit limited capability in forecasting critical progression in patients with multimorbidity. To address this, we propose GATET, a novel architecture that integrates graph neural networks, deep tabular learning, and population subgraph partitioning to improve predictive accuracy for multimorbid patients.

GATET comprises three core modules: (1) Dependency Feature Extraction (DFE), which generates trainable adjacency matrices guided by medical prior knowledge; (2) Attentive Aggregation for Constructing Graphs (CGsA), which employs dual-channel graph attention networks to capture intricate relationships within the population graph, and (3) Feature Weighting based on TabNet (FWT), which preserves TabNet's interpretability while removing its global modeling mechanism to eliminate redundant computations. The implementation is publicly available at https://www.researchgate.net/profile/Chengjie-Li-7.

Extensive repeated experiments with statistical hypothesis testing, performed on clinical data from a tertiary hospital in Southwest China, demonstrate that GATET improves prediction accuracy by approximately 10% over baseline models and achieves superior performance across additional metrics. Domain adaptation experiments on multiple datasets confirm its effectiveness for other disease prediction tasks. Supplementary analyses, including parameter sensitivity studies and graph-aggregated feature selection, empirically validate the importance of age-based stratification in multimorbid populations.

Comprehensive comparative evaluations highlight GATET's strong potential for predicting critical disease progression in multimorbid patients. This work presents an effective strategy for integrating prior medical knowledge into graph-based frameworks, advancing predictive analytics for structured tabular data and delivering tangible improvements for complex clinical prediction.