ArticleMedical & biological engineering & computing2015
Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models.
Article in Medical & biological engineering & computing, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 96 citations in OpenAlex.
- Pooled it
- Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments.Diabetes/metabolism research and reviews · 2021Pooled it
- Hypoglycemia Detection and Carbohydrate Suggestion in an Artificial Pancreas.Journal of diabetes science and technology · 2016Trial
- Temporal gradient analysis of blood glucose responses to non-standard physical activity: a free-living study in type 1 diabetes.Frontiers in sports and active living · 2026Article
- Personalized Blood Glucose Forecasting From Limited CGM Data Using Incrementally Retrained LSTM.IEEE transactions on bio-medical engineering · 2025Article
- Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades.Frontiers in immunology · 2025Review
- Machine learning based study for the classification of Type 2 diabetes mellitus subtypes.BioData mining · 2023Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
- Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review.Sensors (Basel, Switzerland) · 2022Review
- Incorporating Glucose Variability into Glucose Forecasting Accuracy Assessment Using the New Glucose Variability Impact Index and the Prediction Consistency Index: An LSTM Case Example.Journal of diabetes science and technology · 2022Article
- Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients.Scientific reports · 2021Article
- Blood glucose concentration prediction based on VMD-KELM-AdaBoost.Medical & biological engineering & computing · 2021Article
- Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions.Sensors (Basel, Switzerland) · 2021Article
- Predicting Progression Patterns of Type 2 Diabetes using Multi-sensor Measurements.Smart health (Amsterdam, Netherlands) · 2021Article
- Developing an Individual Glucose Prediction Model Using Recurrent Neural Network.Sensors (Basel, Switzerland) · 2020Article
- Deep Physiological Model for Blood Glucose Prediction in T1DM Patients.Sensors (Basel, Switzerland) · 2020Article
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- Feasibility study of portable microwave microstrip open-loop resonator for non-invasive blood glucose level sensing: proof of concept.Medical & biological engineering & computing · 2019Article
- Glucose Concentration Measurement in Human Blood Plasma Solutions with Microwave Sensors.Sensors (Basel, Switzerland) · 2019Article
- Short-term prediction of glucose in type 1 diabetes using kernel adaptive filters.Medical & biological engineering & computing · 2019Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glucose concentration in type 1 diabetes is a function of biological and environmental factors which present high inter-patient variability. The objective of this study is to evaluate a number of features, which are extracted from medical and lifestyle self-monitoring data, with respect to their ability to predict the short-term subcutaneous (s.c.) glucose concentration of an individual. Random forests (RF) and RReliefF algorithms are first employed to rank the candidate feature set. Then, a forward selection procedure follows to build a glucose predictive model, where features are sequentially added to it in decreasing order of importance. Predictions are performed using support vector regression or Gaussian processes. The proposed method is validated on a dataset of 15 type diabetics in real-life conditions. The s.c. glucose profile along with time of the day and plasma insulin concentration are systematically highly ranked, while the effect of food intake and physical activity varies considerably among patients. Moreover, the average prediction error converges in less than d/2 iterations (d is the number of features). Our results suggest that RF and RReliefF can find the most informative features and can be successfully used to customize the input of glucose 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.