mlsanityData to 14 Oct 2026

Structure-Aware Temporal Modeling for Chronic Disease Progression Prediction

arXiv 2508.14942 ↗First posted 20 Aug 2025cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 13 of its 28 references that are in the map. First posted on arXiv 20 Aug 2025.

Builds on

13 of 28 references.

References · 13

  1. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
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  3. 2024Breast Cancer Image Classification Method Based on Deep Transfer Learningcited by 12
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1 more
  1. 2023PULSAR: Graph based Positive Unlabeled Learning with Multi Stream Adaptive Convolutions for Parkinson's Disease Recognitioncited by 1

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Citing papers per year: 2025: 7.

7 papers.

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  2. 2025Contrastive Learning-Based Dependency Modeling for Anomaly Detection in Cloud Servicescited by 9
  3. 2025Multi-Objective Adaptive Rate Limiting in Microservices Using Deep Reinforcement Learningcited by 5
  4. 2025Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Datacited by 5
  5. 2025Machine Learning Approaches to Clinical Risk Prediction: Multi-Scale Temporal Alignment in Electronic Health Recordscited by 2
  6. 2025Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networkscited by 1
  7. 2025Dynamic Anomaly Identification in Accounting Transactions via Multi-Head Self-Attention Networkscited by 1