Structure-Aware Temporal Modeling for Chronic Disease Progression Prediction

TL;DR

This paper introduces a novel structure-aware temporal modeling framework using graph neural networks and Transformers to improve Parkinson's disease progression prediction, capturing complex symptom relationships and temporal dynamics.

Contribution

It presents a unified, scalable framework that integrates structural symptom relationships and temporal features with a gating mechanism, advancing chronic disease progression modeling.

Findings

Outperforms existing models in AUC, RMSE, and IPW-F1 metrics.

Effectively distinguishes disease progression stages.

Enhances personalized symptom trajectory modeling.

Abstract

This study addresses the challenges of symptom evolution complexity and insufficient temporal dependency modeling in Parkinson's disease progression prediction. It proposes a unified prediction framework that integrates structural perception and temporal modeling. The method leverages graph neural networks to model the structural relationships among multimodal clinical symptoms and introduces graph-based representations to capture semantic dependencies between symptoms. It also incorporates a Transformer architecture to model dynamic temporal features during disease progression. To fuse structural and temporal information, a structure-aware gating mechanism is designed to dynamically adjust the fusion weights between structural encodings and temporal features, enhancing the model's ability to identify key progression stages. To improve classification accuracy and stability, the framework includes a multi-component modeling pipeline, consisting of a graph construction module, a temporal encoding module, and a prediction output layer. The model is evaluated on real-world longitudinal Parkinson's disease data. The experiments involve comparisons with mainstream models, sensitivity analysis of hyperparameters, and graph connection density control. Results show that the proposed method outperforms existing approaches in AUC, RMSE, and IPW-F1 metrics. It effectively distinguishes progression stages and improves the model's ability to capture personalized symptom trajectories. The overall framework demonstrates strong generalization and structural scalability, providing reliable support for intelligent modeling of chronic progressive diseases such as Parkinson's disease.