A deep learning approach to adherence detection for type 2 diabetics

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Diabetes has become one of the biggest health problems in the world. In this context, adherence to insulin treatment is essential in order to avoid life-threatening complications. In this pilot study, a novel adherence detection algorithm using Deep Learning (DL) approaches was developed for type 2 diabetes (T2D) patients, based on simulated Continuous Glucose Monitoring (CGM) signals. A large and diverse amount of CGM signals were simulated for T2D patients using a T2D adapted version of the Medtronic Virtual Patient (MVP) model for T1D. By using these signals, different classification algorithms were compared using a comprehensive grid search. We contrast a standard logistic regression baseline to Multi- Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The best classification performance with an average accuracy of 77:5% was achieved with CNN. Hence, this indicates the potential of DL, when considering adherence detection systems for T2D patients.
Original languageEnglish
Title of host publicationProceedings of 2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society
PublisherIEEE
Publication date2017
Pages2896-9
ISBN (Print)978-1-5090-2809-2
DOIs
Publication statusPublished - 2017
Event2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society - JUNGMUN Sightseeing Complex, Jeju Island, Korea, Republic of
Duration: 11 Jul 201715 Jul 2017

Conference

Conference2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society
LocationJUNGMUN Sightseeing Complex
CountryKorea, Republic of
CityJeju Island
Period11/07/201715/07/2017
Series2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (embc)
ISSN1558-4615

Cite this

Mohebbi, A., Aradóttir, T. B., Johansen, A. R., Bengtsson, H., Fraccaro, M., & Mørup, M. (2017). A deep learning approach to adherence detection for type 2 diabetics. In Proceedings of 2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (pp. 2896-9). IEEE. 2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (embc) https://doi.org/10.1109/EMBC.2017.8037462
Mohebbi, Ali ; Aradóttir, Tinna Björk ; Johansen, Alexander Rosenberg ; Bengtsson, Henrik ; Fraccaro, Marco ; Mørup, Morten. / A deep learning approach to adherence detection for type 2 diabetics. Proceedings of 2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society. IEEE, 2017. pp. 2896-9 (2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (embc)).
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title = "A deep learning approach to adherence detection for type 2 diabetics",
abstract = "Diabetes has become one of the biggest health problems in the world. In this context, adherence to insulin treatment is essential in order to avoid life-threatening complications. In this pilot study, a novel adherence detection algorithm using Deep Learning (DL) approaches was developed for type 2 diabetes (T2D) patients, based on simulated Continuous Glucose Monitoring (CGM) signals. A large and diverse amount of CGM signals were simulated for T2D patients using a T2D adapted version of the Medtronic Virtual Patient (MVP) model for T1D. By using these signals, different classification algorithms were compared using a comprehensive grid search. We contrast a standard logistic regression baseline to Multi- Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The best classification performance with an average accuracy of 77:5{\%} was achieved with CNN. Hence, this indicates the potential of DL, when considering adherence detection systems for T2D patients.",
author = "Ali Mohebbi and Arad{\'o}ttir, {Tinna Bj{\"o}rk} and Johansen, {Alexander Rosenberg} and Henrik Bengtsson and Marco Fraccaro and Morten M{\o}rup",
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Mohebbi, A, Aradóttir, TB, Johansen, AR, Bengtsson, H, Fraccaro, M & Mørup, M 2017, A deep learning approach to adherence detection for type 2 diabetics. in Proceedings of 2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society. IEEE, 2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (embc), pp. 2896-9, 2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society, Jeju Island, Korea, Republic of, 11/07/2017. https://doi.org/10.1109/EMBC.2017.8037462

A deep learning approach to adherence detection for type 2 diabetics. / Mohebbi, Ali; Aradóttir, Tinna Björk; Johansen, Alexander Rosenberg; Bengtsson, Henrik; Fraccaro, Marco; Mørup, Morten.

Proceedings of 2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society. IEEE, 2017. p. 2896-9 (2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (embc)).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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AB - Diabetes has become one of the biggest health problems in the world. In this context, adherence to insulin treatment is essential in order to avoid life-threatening complications. In this pilot study, a novel adherence detection algorithm using Deep Learning (DL) approaches was developed for type 2 diabetes (T2D) patients, based on simulated Continuous Glucose Monitoring (CGM) signals. A large and diverse amount of CGM signals were simulated for T2D patients using a T2D adapted version of the Medtronic Virtual Patient (MVP) model for T1D. By using these signals, different classification algorithms were compared using a comprehensive grid search. We contrast a standard logistic regression baseline to Multi- Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The best classification performance with an average accuracy of 77:5% was achieved with CNN. Hence, this indicates the potential of DL, when considering adherence detection systems for T2D patients.

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Mohebbi A, Aradóttir TB, Johansen AR, Bengtsson H, Fraccaro M, Mørup M. A deep learning approach to adherence detection for type 2 diabetics. In Proceedings of 2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society. IEEE. 2017. p. 2896-9. (2017 39th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (embc)). https://doi.org/10.1109/EMBC.2017.8037462