ArticleDiabetes technology & therapeutics2018
Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System.
Article in Diabetes technology & therapeutics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 89 citations in OpenAlex.
- Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study.JMIR mHealth and uHealth · 2022Trial
- On the road to fully automated insulin delivery: A systematic review of meal announcement free algorithms.PLOS digital health · 2026Article
- Performance of continuous glucose monitoring-based meal detection algorithms in young healthy adults.Scientific reports · 2026Article
- Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes.NPJ digital medicine · 2025Article
- Metabolic Models, in Silico Trials, and Algorithms.Diabetes technology & therapeutics · 2025Review
- Metabolic Models, in Silico Trials, and Algorithms.Journal of diabetes science and technology · 2025Review
- Dosing Algorithms for Insulin Pumps.Diabetes spectrum : a publication of the American Diabetes Association · 2025Article
- Mealtime prediction using wearable insulin pump data to support diabetes management.Scientific reports · 2024Article
- An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems.Scientific reports · 2024Article
- Continuous glucose monitoring as an objective measure of meal consumption in individuals with binge-spectrum eating disorders: A proof-of-concept study.European eating disorders review : the journal of the Eating Disorders Association · 2024Article
- Objective Determination of Eating Occasion Timing: Combining Self-Report, Wrist Motion, and Continuous Glucose Monitoring to Detect Eating Occasions in Adults With Prediabetes and Obesity.Journal of diabetes science and technology · 2024Article
- Diabetes management in the era of artificial intelligence.Archives of medical sciences. Atherosclerotic diseases · 2024Article
- Detection of Meals and Physical Activity Events From Free-Living Data of People With Diabetes.Journal of diabetes science and technology · 2023Article
- Multivariable Automated Insulin Delivery System for Handling Planned and Spontaneous Physical Activities.Journal of diabetes science and technology · 2023Article
- Intelligent Insulin vs. Artificial Intelligence for Type 1 Diabetes: Will the Real Winner Please Stand Up?International journal of molecular sciences · 2023Review
- Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review.Frontiers in nutrition · 2023Article
- Review
- Meal and Physical Activity Detection from Free-living Data for Discovering Disturbance Patterns to Glucose Levels in People with Diabetes.BioMedInformatics · 2022Article
- A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems.Sensors (Basel, Switzerland) · 2022Article
- A New Meal Absorption Model for Artificial Pancreas Systems.Journal of diabetes science and technology · 2022Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAutomatically attenuating the postprandial rise in the blood glucose concentration without manual meal announcement is a significant challenge for artificial pancreas (AP) systems. In this study, a meal module is proposed to detect the consumption of a meal and to estimate the amount of carbohydrate (CHO) intake.
methodsThe meals are detected based on qualitative variables describing variation of continuous glucose monitoring (CGM) readings. The CHO content of the meals/snacks is estimated by a fuzzy system using CGM and subcutaneous insulin delivery data. The meal bolus amount is computed according to the patient's insulin to CHO ratio. Integration of the meal module into a multivariable AP system allows revision of estimated CHO based on knowledge about physical activity, sleep, and the risk of hypoglycemia before the final decision for a meal bolus is made.
resultsThe algorithm is evaluated by using 117 meals/snacks in retrospective data from 11 subjects with type 1 diabetes. Sensitivity, defined as the percentage of correctly detected meals and snacks, is 93.5% for meals and 68.0% for snacks. The percentage of false positives, defined as the proportion of false detections relative to the total number of detected meals and snacks, is 20.8%.
conclusionsIntegration of a meal detection module in an AP system is a further step toward an automated AP without manual entries. Detection of a consumed meal/snack and infusion of insulin boluses using an estimate of CHO enables the AP system to automatically prevent postprandial hyperglycemia.
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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.