# Structure-Aware Temporal Modeling for Chronic Disease Progression Prediction

arXiv: https://arxiv.org/abs/2508.14942 · HTML: https://mlsanity.com/paper/2508.14942/

## 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 (most cited first, 13 of 13)

- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example](https://mlsanity.com/paper/2404.08279/) (2024)
- [Breast Cancer Image Classification Method Based on Deep Transfer Learning](https://mlsanity.com/paper/2404.09226/) (2024)
- [Dynamic Operating System Scheduling Using Double DQN: A Reinforcement Learning Approach to Task Optimization](https://mlsanity.com/paper/2503.23659/) (2025)
- [MedKAN: An Advanced Kolmogorov-Arnold Network for Medical Image Classification](https://mlsanity.com/paper/2502.18416/) (2025)
- [Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration](https://mlsanity.com/paper/2504.00282/) (2025)
- [Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference](https://mlsanity.com/paper/2504.05758/) (2025)
- [Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation](https://mlsanity.com/paper/2505.07674/) (2025)
- [Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks](https://mlsanity.com/paper/2505.08220/) (2025)
- [Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2505.05989/) (2025)
- [Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning](https://mlsanity.com/paper/2505.00299/) (2025)
- [AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine](https://mlsanity.com/paper/2311.14304/) (2023)
- [PULSAR: Graph based Positive Unlabeled Learning with Multi Stream Adaptive Convolutions for Parkinson's Disease Recognition](https://mlsanity.com/paper/2312.05780/) (2023)

## Built on by (most cited first, 7 of 7)

- [Trustworthy Summarization via Uncertainty Quantification and Risk Awareness in Large Language Models](https://mlsanity.com/paper/2510.01231/) (2025)
- [Contrastive Learning-Based Dependency Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2510.13368/) (2025)
- [Multi-Objective Adaptive Rate Limiting in Microservices Using Deep Reinforcement Learning](https://mlsanity.com/paper/2511.03279/) (2025)
- [Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Data](https://mlsanity.com/paper/2511.04158/) (2025)
- [Machine Learning Approaches to Clinical Risk Prediction: Multi-Scale Temporal Alignment in Electronic Health Records](https://mlsanity.com/paper/2511.21561/) (2025)
- [Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networks](https://mlsanity.com/paper/2509.04973/) (2025)
- [Dynamic Anomaly Identification in Accounting Transactions via Multi-Head Self-Attention Networks](https://mlsanity.com/paper/2511.12122/) (2025)

## 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.