ArticleJournal of diabetes science and technology2018
A Neural-Network-Based Approach to Personalize Insulin Bolus Calculation Using Continuous Glucose Monitoring.
Article in Journal of diabetes science and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 62 citations in OpenAlex.
- Recent advances in the precision control strategy of artificial pancreas.Medical & biological engineering & computing · 2024Review
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
- Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems.Bioengineering (Basel, Switzerland) · 2022Article
- Predicting Blood Glucose Concentration after Short-Acting Insulin Injection Using Discontinuous Injection Records.Sensors (Basel, Switzerland) · 2022Article
- Machine learning for initial insulin estimation in hospitalized patients.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction.Journal of diabetes science and technology · 2021Article
- LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management.Sensors (Basel, Switzerland) · 2021Article
- Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study.JMIR diabetes · 2021Article
- Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes.Current diabetes reports · 2020Review
- An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning.Sensors (Basel, Switzerland) · 2020Article
- Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors.Sensors (Basel, Switzerland) · 2020Review
- Application of Artificial Intelligence Techniques for the Estimation of Basal Insulin in Patients with Type I Diabetes.International journal of endocrinology · 2020Article
- Development of an Error Model for a Factory-Calibrated Continuous Glucose Monitoring Sensor with 10-Day Lifetime.Sensors (Basel, Switzerland) · 2019Article
- Detection of Insulin Pump Malfunctioning to Improve Safety in Artificial Pancreas Using Unsupervised Algorithms.Journal of diabetes science and technology · 2019Article
- Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications.Diabetes & metabolism journal · 2019Review
- Classification of Postprandial Glycemic Status with Application to Insulin Dosing in Type 1 Diabetes-An In Silico Proof-of-Concept.Sensors (Basel, Switzerland) · 2019Article
- Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy.Diabetes technology & therapeutics · 2019Article
- Comprehensive Analysis of the Real Lifestyles of T1D Patients for the Purpose of Designing a Personalized Counselor for Prandial Insulin Dosing.Nutrients · 2019Article
- Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks.International journal of environmental research and public health · 2019Article
- In Silico Assessment of Literature Insulin Bolus Calculation Methods Accounting for Glucose Rate of Change.Journal of diabetes science and technology · 2019Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn type 1 diabetes (T1D) therapy, the calculation of the meal insulin bolus is performed according to a standard formula (SF) exploiting carbohydrate intake, carbohydrate-to-insulin ratio, correction factor, insulin on board, and target glucose. Recently, some approaches were proposed to account for preprandial glucose rate of change (ROC) in the SF, including those by Scheiner and by Pettus and Edelman. Here, the aim is to develop a new approach, based on neural networks (NN), to optimize and personalize the bolus calculation using continuous glucose monitoring information and some easily accessible patient parameters.
methodThe UVa/Padova T1D Simulator was used to simulate data of 100 virtual adults in a single-meal noise-free scenario with different conditions in terms of meal amount and preprandial blood glucose and ROC values. An NN was trained to learn the optimal insulin dose using the SF parameters, ROC, body weight, insulin pump basal infusion rate and insulin sensitivity as features. The performance of the NN for meal bolus calculation was assessed by blood glucose risk index (BGRI) and compared to the methods by Scheiner and by Pettus and Edelman.
resultsThe NN approach brings to a small but statistically significant ( P < .001) reduction of BGRI value, equal to 0.37, 0.23, and 0.20 versus SF, Scheiner, and Pettus and Edelman, respectively.
conclusionThis preliminary study showed the potentiality of using NNs for the personalization and optimization of the meal insulin bolus calculation. Future work will deal with more realistic scenarios including technological and physiological/behavioral sources of variability.
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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.