ArticleJournal of diabetes science and technology2013
An early warning system for hypoglycemic/hyperglycemic events based on fusion of adaptive prediction models.
Article in Journal of diabetes science and technology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments.Diabetes/metabolism research and reviews · 2021Pooled it
- Combined Use of Glucose-Specific Model Identification and Alarm Strategy Based on Prediction-Funnel to Improve Online Forecasting of Hypoglycemic Events.Journal of diabetes science and technology · 2023Article
- Machine Learning Prediction of Hypoglycemia and Hyperglycemia From Electronic Health Records: Algorithm Development and Validation.JMIR formative research · 2022Article
- Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults With Type 2 Diabetes Using One Drop: Retrospective Cohort Study.JMIR diabetes · 2022Article
- GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes.Medical & biological engineering & computing · 2022Review
- Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction.BMC medical informatics and decision making · 2021Article
- Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only.Sensors (Basel, Switzerland) · 2021Article
- Artificial Intelligence in Decision Support Systems for Type 1 Diabetes.Sensors (Basel, Switzerland) · 2020Review
- Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes.Journal of medical Internet research · 2019Review
- Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.Journal of medical Internet research · 2018Review
- Predicting the 6-month risk of severe hypoglycemia among adults with diabetes: Development and external validation of a prediction model.Journal of diabetes and its complications · 2017Article
- Model of glucose sensor error components: identification and assessment for new Dexcom G4 generation devices.Medical & biological engineering & computing · 2015Article
- Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models.Medical & biological engineering & computing · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionEarly warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia/hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy.
methodsThe EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models' outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS.
resultsThe EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms.
conclusionCombined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.
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