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Sensor Technologies: Artificial Intelligence for Diabetes Management
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Sensor Technologies: Artificial Intelligence for Diabetes Management

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 26942

Special Issue Editors

Dr. Josep Vehí

E-Mail Website
Guest Editor
1. Department of Electric and Electronic Engineering, University of Girona, 17003 Girona, Spain
2. Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 28001 Madrid, Spain
Interests: artificial pancreas; modelling and control in biomedicine; machine learning applications to biomedicine; robust control; fault tolerant control
Special Issues, Collections and Topics in MDPI journals
Dr. Razvan Bunescu

E-Mail Website
Guest Editor
Stocker 341, School of Electrical Engineering and Computer Science. Ohio University. Athens, OH, 45701. USA
Interests: machine learning; biomedical informatics; natural language processing; music analysis; computational creativity
Dr. Pau Herrero

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Interests: diabetes technology; control engineering; machine learning; biomedical informatics; infectious diseases technology
Special Issues, Collections and Topics in MDPI journals
Dr. Iván Contreras

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering. University of GIrona. Campus Montilivi, edifici P4, 17003 Spain
Interests: artificial intelligence; machine learning; computer architectures; parallel programming; biomedicine applications

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is a rapidly growing field, and its applications for diabetes research are growing even faster. Due to their ability to efficiently process large amounts of data and find complex patterns, AI methods are attracting significant interest in the field of diabetes management, where electronic data acquired from people with diabetes have grown exponentially. The convergence of promising AI methods with the latest technological advances in medical devices, mobile computing and sensor technologies is expected to facilitate the creation and provision of better healthcare services for the management of chronic diseases such as diabetes.

We are proud to announce a Special Issue entitled “Sensor Technologies: Artificial Intelligence for Diabetes Management”. This issue aims to present state-of-the-art research on the use of artificial intelligence techniques for decision support systems (DSS) in diabetes management, and also examples of what may await us in the near future in the intelligent management of diabetes.

Dr. Josep Vehí
Dr. Razvan Bunescu
Dr. Pau Herrero
Dr. Iván Contreras
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • diabetes management
  • artificial intelligence
  • clinical decision support
  • machine learning
  • diabetes technology
  • wearables
  • IoT
  • ambient intelligence

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Published Papers (4 papers)

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Research

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22 pages, 762 KiB  
Article
LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management
by Jeremy Beauchamp, Razvan Bunescu, Cindy Marling, Zhongen Li and Chang Liu
Sensors 2021, 21(9), 3303; https://doi.org/10.3390/s21093303 - 10 May 2021
Cited by 8 | Viewed by 3661
Abstract
To avoid serious diabetic complications, people with type 1 diabetes must keep their blood glucose levels (BGLs) as close to normal as possible. Insulin dosages and carbohydrate consumption are important considerations in managing BGLs. Since the 1960s, models have been developed to forecast [...] Read more.
To avoid serious diabetic complications, people with type 1 diabetes must keep their blood glucose levels (BGLs) as close to normal as possible. Insulin dosages and carbohydrate consumption are important considerations in managing BGLs. Since the 1960s, models have been developed to forecast blood glucose levels based on the history of BGLs, insulin dosages, carbohydrate intake, and other physiological and lifestyle factors. Such predictions can be used to alert people of impending unsafe BGLs or to control insulin flow in an artificial pancreas. In past work, we have introduced an LSTM-based approach to blood glucose level prediction aimed at “what-if” scenarios, in which people could enter foods they might eat or insulin amounts they might take and then see the effect on future BGLs. In this work, we invert the “what-if” scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future. Leveraging a recent state-of-the-art model for time series forecasting, we then derive a novel architecture for the same recommendation task, in which the two LSTM chain is used as a repeating block inside a deep residual architecture. Experimental evaluations using real patient data from the OhioT1DM dataset show that the new integrated architecture compares favorably with the previous LSTM-based approach, substantially outperforming the baselines. The promising results suggest that this novel approach could potentially be of practical use to people with type 1 diabetes for self-management of BGLs. Full article
(This article belongs to the Special Issue Sensor Technologies: Artificial Intelligence for Diabetes Management)
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15 pages, 1336 KiB  
Article
An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning
by Taiyu Zhu, Kezhi Li, Lei Kuang, Pau Herrero and Pantelis Georgiou
Sensors 2020, 20(18), 5058; https://doi.org/10.3390/s20185058 - 6 Sep 2020
Cited by 45 | Viewed by 6242
Abstract
(1) Background: People living with type 1 diabetes (T1D) require self-management to maintain blood glucose (BG) levels in a therapeutic range through the delivery of exogenous insulin. However, due to the various variability, uncertainty and complex glucose dynamics, optimizing the doses of insulin [...] Read more.
(1) Background: People living with type 1 diabetes (T1D) require self-management to maintain blood glucose (BG) levels in a therapeutic range through the delivery of exogenous insulin. However, due to the various variability, uncertainty and complex glucose dynamics, optimizing the doses of insulin delivery to minimize the risk of hyperglycemia and hypoglycemia is still an open problem. (2) Methods: In this work, we propose a novel insulin bolus advisor which uses deep reinforcement learning (DRL) and continuous glucose monitoring to optimize insulin dosing at mealtime. In particular, an actor-critic model based on deep deterministic policy gradient is designed to compute mealtime insulin doses. The proposed system architecture uses a two-step learning framework, in which a population model is first obtained and then personalized by subject-specific data. Prioritized memory replay is adopted to accelerate the training process in clinical practice. To validate the algorithm, we employ a customized version of the FDA-accepted UVA/Padova T1D simulator to perform in silico trials on 10 adult subjects and 10 adolescent subjects. (3) Results: Compared to a standard bolus calculator as the baseline, the DRL insulin bolus advisor significantly improved the average percentage time in target range (70–180 mg/dL) from 74.1%±8.4% to 80.9%±6.9% (p<0.01) and 54.9%±12.4% to 61.6%±14.1% (p<0.01) in the the adult and adolescent cohorts, respectively, while reducing hypoglycemia. (4) Conclusions: The proposed algorithm has the potential to improve mealtime bolus insulin delivery in people with T1D and is a feasible candidate for future clinical validation. Full article
(This article belongs to the Special Issue Sensor Technologies: Artificial Intelligence for Diabetes Management)
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11 pages, 252 KiB  
Article
Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor
by Arthur Bertachi, Clara Viñals, Lyvia Biagi, Ivan Contreras, Josep Vehí, Ignacio Conget and Marga Giménez
Sensors 2020, 20(6), 1705; https://doi.org/10.3390/s20061705 - 19 Mar 2020
Cited by 52 | Viewed by 5811
Abstract
(1) Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients [...] Read more.
(1) Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients on MDI. (2) Methods: ten T1D adults were studied during 12 weeks. Information regarding T1D management, continuous glucose monitoring (CGM), and from a physical activity tracker were obtained under free-living conditions at home. Supervised machine-learning algorithms were applied to the data, and prediction models were created to forecast the occurrence of NH. Individualized prediction models were generated using multilayer perceptron (MLP) and a support vector machine (SVM). (3) Results: population outcomes indicated that more than 70% of the NH may be avoided with the proposed methodology. The predictions performed by the SVM achieved the best population outcomes, with a sensitivity and specificity of 78.75% and 82.15%, respectively. (4) Conclusions: our study supports the feasibility of using ML techniques to address the prediction of nocturnal hypoglycemia in the daily life of patients with T1D on MDI, using CGM and a physical activity tracker. Full article
(This article belongs to the Special Issue Sensor Technologies: Artificial Intelligence for Diabetes Management)
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Review

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26 pages, 708 KiB  
Review
Artificial Intelligence in Decision Support Systems for Type 1 Diabetes
by Nichole S. Tyler and Peter G. Jacobs
Sensors 2020, 20(11), 3214; https://doi.org/10.3390/s20113214 - 5 Jun 2020
Cited by 55 | Viewed by 9644
Abstract
Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent [...] Read more.
Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems. Full article
(This article belongs to the Special Issue Sensor Technologies: Artificial Intelligence for Diabetes Management)
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