Graph-Enhanced Multi-Task Learning for Type 2 Diabetes Comorbidity Risk Prediction

Early prediction of Type 2 diabetes mellitus (T2DM) complications holds significant clinical importance for improving patient outcomes and reducing healthcare burden, yet existing prediction methods exhibit notable limitations. This paper proposes a Graph-Enhanced Multi-Task Learning (GEMTL) framework for simultaneously predicting the occurrence risk of multiple diabetic complications. The framework constructs disease relation graphs through a hybrid strategy that linearly combines a data-driven statistical graph derived from disease co-occurrence patterns with a knowledge-driven prior graph encoding clinically established association strengths, employs graph neural networks to capture higher-order dependencies among diseases, designs cross-attention mechanisms to achieve heterogeneous information fusion between patient features and disease graph embeddings, and utilizes multi-gating expert network architecture for task-specific modeling. Large-scale experimental validation was conducted on a dataset constructed from MIMIC-IV. Results demonstrate that the GEMTL framework achieves macro-averaged F1 score of 0.723, micro-averaged F1 score of 0.856, and mean Average Precision of 0.759, significantly outperforming baseline methods across all evaluation metrics, including traditional machine learning methods, deep multi-task learning methods, graph neural network methods, and multi-expert architectures. This study provides an effective technical framework for complex medical multi-task prediction problems, with broad application prospects in diabetes precision management and clinical decision support.