Zhu, T;
Li, K;
Herrero, P;
Georgiou, P;
(2020)
Deep Learning for Diabetes: A Systematic Review.
IEEE Journal of Biomedical and Health Informatics
10.1109/JBHI.2020.3040225.
(In press).
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Abstract
Diabetes is a chronic metabolic disorder that affects an estimated 463 million people worldwide. Aiming to improve the treatment of people with diabetes, digital health has been widely adopted in recent years and generated a huge amount of data that could be used for further management of this chronic disease. Taking advantage of this, approaches that use artificial intelligence and specifically deep learning, an emerging type of machine learning, have been widely adopted with promising results. In this paper, we present a comprehensive review of the applications of deep learning within the field of diabetes. We conducted a systematic literature search and identified three main areas that use this approach: diagnosis of diabetes, glucose management, and diagnosis of diabetes-related complications. The search resulted in the selection of 40 original research articles, of which we have summarized the key information about the employed learning models, development process, main outcomes, and baseline methods for performance evaluation. Among the analyzed literature, it is to be noted that various deep learning techniques and frameworks have achieved stateof-the-art performance in many diabetes-related tasks by outperforming conventional machine learning approaches. Meanwhile, we identify some limitations in the current literature, such as a lack of data availability and model interpretability. The rapid developments in deep learning and the increase in available data offer the possibility to meet these challenges in the near future and allow the widespread deployment of this technology in clinical settings.
| Type: | Article |
|---|---|
| Title: | Deep Learning for Diabetes: A Systematic Review |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/JBHI.2020.3040225 |
| Publisher version: | https://doi.org/10.1109/JBHI.2020.3040225 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Diabetes, deep learning, deep neural networks, glucose management, diabetic complications, artificial intelligence |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10117480 |
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