Kavli Affiliate: Ting Xu
| First 5 Authors: Jiacheng Hu, Jiacheng Hu, , ,
| Summary:
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.
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