Federated Semi-Supervised Graph Neural Networks with Prototype-Guided Pseudo-Labeling for Privacy-Preserving Gestational Diabetes Mellitus Prediction
📰 ArXiv cs.AI
Learn how to apply federated semi-supervised graph neural networks with prototype-guided pseudo-labeling for privacy-preserving gestational diabetes mellitus prediction
Action Steps
- Build a federated learning framework to enable collaborative training of graph neural networks without sharing patient-level data
- Apply prototype-guided pseudo-labeling to generate high-quality pseudo-labels for unlabeled electronic health records (EHRs)
- Configure graph neural networks to learn effective representations of EHR data
- Test the performance of the federated semi-supervised graph neural networks on a real-world dataset
- Compare the results with traditional supervised learning approaches to evaluate the effectiveness of the proposed method
Who Needs to Know This
Data scientists and machine learning engineers working on healthcare projects can benefit from this approach to improve the accuracy of gestational diabetes mellitus prediction while preserving patient data privacy
Key Insight
💡 Federated semi-supervised graph neural networks with prototype-guided pseudo-labeling can improve the accuracy of gestational diabetes mellitus prediction while preserving patient data privacy
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🚀 Federated semi-supervised graph neural networks for privacy-preserving gestational diabetes mellitus prediction! 📊💻
Key Takeaways
Learn how to apply federated semi-supervised graph neural networks with prototype-guided pseudo-labeling for privacy-preserving gestational diabetes mellitus prediction
Full Article
Title: Federated Semi-Supervised Graph Neural Networks with Prototype-Guided Pseudo-Labeling for Privacy-Preserving Gestational Diabetes Mellitus Prediction
Abstract:
arXiv:2605.01810v1 Announce Type: cross Abstract: Gestational Diabetes Mellitus (GDM) is a high-prevalence pregnancy complication that requires accurate early risk stratification to reduce maternal and fetal morbidity. However, real-world clinical deployment of machine learning is hindered by two coupled constraints: (i) label scarcity, where a large fraction of electronic health records (EHR) lack confirmed diagnostic labels, and (ii) data privacy, which prevents sharing patient-level data acro
Abstract:
arXiv:2605.01810v1 Announce Type: cross Abstract: Gestational Diabetes Mellitus (GDM) is a high-prevalence pregnancy complication that requires accurate early risk stratification to reduce maternal and fetal morbidity. However, real-world clinical deployment of machine learning is hindered by two coupled constraints: (i) label scarcity, where a large fraction of electronic health records (EHR) lack confirmed diagnostic labels, and (ii) data privacy, which prevents sharing patient-level data acro
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