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

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

Deadline for manuscript submissions: closed (30 August 2022) | Viewed by 3396

Special Issue Editor

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

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
Guest Editor

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

Published Papers (1 paper)

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Research

14 pages, 1988 KiB  
Article
A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin
by Adrià Parcerisas, Ivan Contreras, Alexia Delecourt, Arthur Bertachi, Aleix Beneyto, Ignacio Conget, Clara Viñals, Marga Giménez and Josep Vehi
Sensors 2022, 22(4), 1665; https://doi.org/10.3390/s22041665 - 21 Feb 2022
Cited by 12 | Viewed by 2689
Abstract
Nocturnal hypoglycemia (NH) is one of the most challenging events for multiple dose insulin therapy (MDI) in people with type 1 diabetes (T1D). The goal of this study is to design a method to reduce the incidence of NH in people with T1D [...] Read more.
Nocturnal hypoglycemia (NH) is one of the most challenging events for multiple dose insulin therapy (MDI) in people with type 1 diabetes (T1D). The goal of this study is to design a method to reduce the incidence of NH in people with T1D under MDI therapy, providing a decision-support system and improving confidence toward self-management of the disease considering the dataset used by Bertachi et al. Different machine learning (ML) algorithms, data sources, optimization metrics and mitigation measures to predict and avoid NH events have been studied. In addition, we have designed population and personalized models and studied the generalizability of the models and the influence of physical activity (PA) on them. Obtaining 30 g of rescue carbohydrates (CHO) is the optimal value for preventing NH, so it can be asserted that this is the value with which the time under 70 mg/dL decreases the most, with almost a 35% reduction, while increasing the time in the target range by 1.3%. This study supports the feasibility of using ML techniques to address the prediction of NH in patients with T1D under MDI therapy, using continuous glucose monitoring (CGM) and a PA tracker. The results obtained prove that BG predictions can not only be critical in achieving safer diabetes management, but also assist physicians and patients to make better and safer decisions regarding insulin therapy and their day-to-day lives. Full article
(This article belongs to the Special Issue Sensor Technologies: Artificial Intelligence for Diabetes Management II)
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