ArticleJournal of diabetes science and technology2022
A Conditional Generative Adversarial Network for Synthesis of Continuous Glucose Monitoring Signals.
Article in Journal of diabetes science and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Continuous Glucose Monitoring Data Compression Using Peak-Nadir Encoding in Diabetes: Method Development and Evaluation.JMIR biomedical engineering · 2026Article
- Personalised Blood Glucose Time Series Forecasting in Type 1 Diabetes: Deep Collaborative Adversarial Learning.Journal of personalized medicine · 2026Article
- Generative artificial intelligence in diabetes healthcare.iScience · 2025Review
- Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation.Journal of diabetes science and technology · 2025Article
- Generative deep learning for the development of a type 1 diabetes simulator.Communications medicine · 2024Article
- Publicly Available Data Set Including Continuous Glucose Monitoring Data.Journal of diabetes science and technology · 2023Article
- Generative Adversarial Networks in Medicine: Important Considerations for this Emerging Innovation in Artificial Intelligence.Annals of biomedical engineering · 2023Review
- Hypoglycemia event prediction from CGM using ensemble learning.Frontiers in clinical diabetes and healthcare · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This report describes how a Conditional Generative Adversarial Network (CGAN) was used to synthesize realistic continuous glucose monitoring systems (CGM) from healthy individuals and individuals with type 1 diabetes over a range of different HbA1c levels. The results showed that even though the CGAN generated data, did not perfectly reflect real world CGM, many of the important features were captured and reflected in the synthetic signals. It is briefly discussed how heterogenous data sources constitutes a challenge for comparison of predictive CGM models. Therefore 40,000 CGM days were generated by the trained CGAN, equivalent to 940,000 hours of synthetic CGM measurements. These data have been made available in a public database, which can be used as a reference in future studies.
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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.