ArticleSmart health (Amsterdam, Netherlands)2021
Predicting Progression Patterns of Type 2 Diabetes using Multi-sensor Measurements.
Article in Smart health (Amsterdam, Netherlands), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes.NPJ digital medicine · 2025Article
- A systematic review on artificial intelligence approaches for smart health devices.PeerJ. Computer science · 2024Review
- An early warning model of type 2 diabetes risk based on POI visit history and food access management.PloS one · 2023Article
- Obesity Prediction with EHR Data: A deep learning approach with interpretable elements.ACM transactions on computing for healthcare · 2022Article
- Transformer-based Multi-target Regression on Electronic Health Records for Primordial Prevention of Cardiovascular Disease.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2021Article
- Need for an Artificial Intelligence-based Diabetes Care Management System in India and the United States.Health services research and managerial epidemiologyArticle
- Flexible-Window Predictions on Electronic Health Records.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial IntelligenceArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Type 2 diabetes - a prevalent chronic disease worldwide - increases risk for serious health consequences including heart and kidney disease. Forecasting diabetes progression can inform disease management strategies, thereby potentially reducing the likelihood or severity of its consequences. We use continuous glucose monitoring and actigraphy data from 54 individuals with Type 2 diabetes to predict their future hemoglobin A1c, HDL cholesterol, LDL cholesterol, and triglyceride levels one year later. We use a combination of convolutional and recurrent neural networks to develop a deep neural network architecture that can learn the dynamic patterns in different sensors' data and combine those patterns with additional demographic and lab data. To further demonstrate the generalizability of our models, we also evaluate their performance using an independent public dataset of individuals with Type 1 diabetes. In addition to diabetes, our approach could be useful for other serious and chronic physical illness, where dynamic (e.g., from multiple sensors) and static (e.g., demographic) data are used for creating predictive models.
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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.