ArticleScientific reports2021
A weighted patient network-based framework for predicting chronic diseases using graph neural networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 77 citations in OpenAlex.
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- An Explainable AI Framework for Corneal Imaging Interpretation and Refractive Surgery Decision Support.Bioengineering (Basel, Switzerland) · 2025Article
- Extracting Multifaceted Characteristics of Patients With Chronic Disease Comorbidity: Framework Development Using Large Language Models.JMIR medical informatics · 2025Article
- Prediction model for type 2 diabetes mellitus and its association with mortality using machine learning in three independent cohorts from South Korea, Japan, and the UK: a model development and validation study.EClinicalMedicine · 2025Article
- DAPNet: multi-view graph contrastive network incorporating disease clinical and molecular associations for disease progression prediction.BMC medical informatics and decision making · 2024Article
- Comorbidity-based framework for Alzheimer's disease classification using graph neural networks.Scientific reports · 2024Article
- Med-MGF: multi-level graph-based framework for handling medical data imbalance and representation.BMC medical informatics and decision making · 2024Article
- TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2024Article
- Graph Artificial Intelligence in Medicine.Annual review of biomedical data science · 2024Review
- Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study.JMIR aging · 2024Article
- Developing a novel causal inference algorithm for personalized biomedical causal graph learning using meta machine learning.BMC medical informatics and decision making · 2024Article
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- Cardiovascular disease prediction model based on patient behavior patterns in the context of deep learning: a time-series data analysis perspective.Frontiers in psychiatry · 2024Article
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- Probabilistic Graphical Models for Evaluating the Utility of Data-Driven ICD Code Categories in Pediatric Sepsis.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Embedding-based link predictions to explore latent comorbidity of chronic diseases.Health information science and systems · 2023Article
- KAMPNet: multi-source medical knowledge augmented medication prediction network with multi-level graph contrastive learning.BMC medical informatics and decision making · 2023Article
- Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning.BMC medical informatics and decision making · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic disease prediction is a critical task in healthcare. Existing studies fulfil this requirement by employing machine learning techniques based on patient features, but they suffer from high dimensional data problems and a high level of bias. We propose a framework for predicting chronic disease based on Graph Neural Networks (GNNs) to address these issues. We begin by projecting a patient-disease bipartite graph to create a weighted patient network (WPN) that extracts the latent relationship among patients. We then use GNN-based techniques to build prediction models. These models use features extracted from WPN to create robust patient representations for chronic disease prediction. We compare the output of GNN-based models to machine learning methods by using cardiovascular disease and chronic pulmonary disease. The results show that our framework enhances the accuracy of chronic disease prediction. The model with attention mechanisms achieves an accuracy of 93.49% for cardiovascular disease prediction and 89.15% for chronic pulmonary disease prediction. Furthermore, the visualisation of the last hidden layers of GNN-based models shows the pattern for the two cohorts, demonstrating the discriminative strength of the framework. The proposed framework can help stakeholders improve health management systems for patients at risk of developing chronic diseases and conditions.
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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.