ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2021
Interpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging data.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Hierarchical sparse spatiotemporal graph neural network for brain graph classification.iScience · 2026Article
- Graph Neural Networks for Medical Imaging Analysis and Biological Data: Integrating Topology, Geometry, Radiomics, and Generative AI.Bioengineering (Basel, Switzerland) · 2026Review
- Next generation preventive neurology: how artificial intelligence and machine learning are reshaping Alzheimer's disease research.Behavioral and brain functions : BBF · 2026Review
- Interpretable Network-Level Biomarker Discovery for Alzheimer's Stage Assessment Using Resting-State fNIRS Complexity Graphs.Brain sciences · 2026Article
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- Explainable Artificial Intelligence in Neuroimaging of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances.Frontiers in neuroscience · 2025Review
- Dynamically weighted graph neural network for detection of early mild cognitive impairment.PloS one · 2025Article
- An Evolutionary Federated Learning Approach to Diagnose Alzheimer's Disease Under Uncertainty.Diagnostics (Basel, Switzerland) · 2025Article
- Machine Learning Models and Applications for Early Detection.Sensors (Basel, Switzerland) · 2024Review
- Deep learning imaging phenotype can classify metabolic syndrome and is predictive of cardiometabolic disorders.Journal of translational medicine · 2024Article
- An Explainable AI Paradigm for Alzheimer's Diagnosis Using Deep Transfer Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Advanced interpretable diagnosis of Alzheimer's disease using SECNN-RF framework with explainable AI.Frontiers in artificial intelligence · 2024Article
- Developing Explainable Deep Model for Discovering Novel Control Mechanism of Neuro-Dynamics.IEEE transactions on medical imaging · 2024Article
- The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook.Brain sciences · 2023Review
- Personalized Explanations for Early Diagnosis of Alzheimer's Disease Using Explainable Graph Neural Networks with Population Graphs.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder characterized by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an important research topic, and numerous machine learning methods have been proposed to solve this problem. However, traditional machine learning models are facing challenges in effectively integrating longitudinal neuroimaging data and biologically meaningful structure and knowledge to build accurate and interpretable prognostic predictors. To bridge this gap, we propose an interpretable graph neural network (GNN) model for AD prognostic prediction based on longitudinal neuroimaging data while embracing the valuable knowledge of structural brain connectivity. In our empirical study, we demonstrate that 1) the proposed model outperforms several competing models (i.e., DNN, SVM) in terms of prognostic prediction accuracy, and 2) our model can capture neuroanatomical contribution to the prognostic predictor and yield biologically meaningful interpretation to facilitate better mechanistic understanding of the Alzheimer's disease. Source code is available at https://github.com/JaesikKim/temporal-GNN.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.