ArticleSensors (Basel, Switzerland)2020
Deep Physiological Model for Blood Glucose Prediction in T1DM Patients.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.
- Pooled it
- Noninvasive Continuous Glucose Monitoring Using Multimodal Near-Infrared, Temperature, and Pressure Signals on the Earlobe.Biosensors · 2025Article
- Intelligent glucose management in hospitalized patients: Short-term glucose and adverse events prediction.PloS one · 2025Article
- Association of Blood Glucose Data With Physiological and Nutritional Data From Dietary Surveys and Wearable Devices: Database Analysis.JMIR diabetes · 2024Article
- Beyond Expected Patterns in Insulin Needs of People With Type 1 Diabetes: Temporal Analysis of Automated Insulin Delivery Data.JMIRx med · 2024Article
- Article
- Generative deep learning for the development of a type 1 diabetes simulator.Communications medicine · 2024Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
- Type 1 Diabetes Hypoglycemia Prediction Algorithms: Systematic Review.JMIR diabetes · 2022Review
- Diabetes Technology Meeting 2021.Journal of diabetes science and technology · 2022Article
- A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia.Medical & biological engineering & computing · 2021Article
- Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes.Sensors (Basel, Switzerland) · 2021Article
- Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions.Sensors (Basel, Switzerland) · 2021Article
- Learning Carbohydrate Digestion and Insulin Absorption Curves Using Blood Glucose Level Prediction and Deep Learning Models.Sensors (Basel, Switzerland) · 2021Article
- Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study.JMIR diabetes · 2021Article
- Diabetes Mellitus Prediction and Severity Level Estimation Using OWDANN Algorithm.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Accurate estimations for the near future levels of blood glucose are crucial for Type 1 Diabetes Mellitus (T1DM) patients in order to be able to react on time and avoid hypo and hyper-glycemic episodes. Accurate predictions for blood glucose are the base for control algorithms in glucose regulating systems such as the artificial pancreas. Numerous research studies have already been conducted in order to provide predictions for blood glucose levels with particularities in the input signals and underlying models used. These models can be categorized into two major families: those based on tuning glucose physiological-metabolic models and those based on learning glucose evolution patterns based on machine learning techniques. This paper reviews the state of the art in blood glucose predictions for T1DM patients and proposes, implements, validates and compares a new hybrid model that decomposes a deep machine learning model in order to mimic the metabolic behavior of physiological blood glucose methods. The differential equations for carbohydrate and insulin absorption in physiological models are modeled using a Recurrent Neural Network (RNN) implemented using Long Short-Term Memory (LSTM) cells. The results show Root Mean Square Error (RMSE) values under 5 mg/dL for simulated patients and under 10 mg/dL for real patients.
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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.