DGHME:A Hierarchical Hybrid Expert Multi-task Learning Model for Disease Grouping in Diabetes Complication Prediction

Abstract

Accurate identification and prediction of diabetes complications contribute to improved patient health. However, existing prediction models predominantly employ single-task learning (STL) paradigms, failing to fully leverage the intrinsic correlations among different complications that arise from shared underlying pathophysiological mechanisms, thereby limiting predictive accuracy. To address this, we propose a disease-grouping hierarchical mixed expert model (DGHME). This model integrates clinical-pathological grouping knowledge into a multi-task learning (MTL) architecture, constructing a hierarchical network comprising a bottom-layer self-attention shared expert, group-internal shared experts, task-private experts, and a global shared expert. Through an adaptive gating mechanism and uncertainty-based loss weighting strategy, it achieves refined learning of both disease-common and disease-specific features. Finally, comparative experiments against existing multi-task baseline models on a real-world dataset demonstrate the superiority of the proposed DGHME. Our ablation studies further indicate that DGHME aids in accurately identifying high-risk patients and enables more effective complication prediction.

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