Deep learning methods for improving diabetes management tools

Diabetes is a chronic disease that is characterised by a lack of regulation of blood glucose concentration in the body, and thus elevated blood glucose levels. Consequently, affected individuals can experience extreme variations in their blood glucose levels with exogenous insulin treatment. This has associated debilitating short-term and long-term complications that affect quality of life and can result in death in the worst instance. The development of technologies such as glucose meters and, more recently, continuous glucose monitors have offered the opportunity to develop systems towards improving clinical outcomes for individuals with diabetes through better glucose control. Data-driven methods can enable the development of the next generation of diabetes management tools focused on i) informativeness ii) safety and iii) easing the burden of management. This thesis aims to propose deep learning methods for improving the functionality of the variety of diabetes technology tools available for self-management. In the pursuit of the aforementioned goals, a number of deep learning methods are developed and geared towards improving the functionality of the existing diabetes technology tools, generally classified as i) self-monitoring of blood glucose ii) decision support systems and iii) artificial pancreas. These frameworks are primarily based on the prediction of glucose concentration levels. The first deep learning framework we propose is geared towards improving the artificial pancreas and decision support systems that rely on continuous glucose monitors. We first propose a convolutional recurrent neural network (CRNN) in order to forecast the glucose concentration levels over both short-term and long-term horizons. The predictive accuracy of this model outperforms those of traditional data-driven approaches. The feasibility of this proposed approach for ambulatory use is then demonstrated with the implementation of a decision support system on a smartphone application. We further extend CRNNs to the multitask setting to explore the effectiveness of leveraging population data for developing personalised models with limited individual data. We show that this enables earlier deployment of applications without significantly compromising performance and safety. The next challenge focuses on easing the burden of management by proposing a deep learning framework for automatic meal detection and estimation. The deep learning framework presented employs multitask learning and quantile regression to safely detect and estimate the size of unannounced meals with high precision. We also demonstrate that this facilitates automated insulin delivery for the artificial pancreas system, improving glycaemic control without significantly increasing the risk or incidence of hypoglycaemia. Finally, the focus shifts to improving self-monitoring of blood glucose (SMBG) with glucose meters. We propose an uncertainty-aware deep learning model based on a joint Gaussian Process and deep learning framework to provide end users with more dynamic and continuous information similar to continuous glucose sensors. Consequently, we show significant improvement in hyperglycaemia detection compared to the standard SMBG. We hope that through these methods, we can achieve a more equitable improvement in usability and clinical outcomes for individuals with diabetes.Open Acces

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.

Background and Aims: Continuous glucose monitoring (CGM) devices could be useful for real-time management of diabetes therapy. In particular, CGM information could be used in real time to predict future glucose levels in order to prevent hypo-/hyperglycemic events. This article proposes a new online method for predicting future glucose concentration levels from CGM data. Methods: The predictor is implemented with an artificial neural network model (NNM). The inputs of the NNM are the values provided by the CGM sensor during the preceding 20 min, while the output is the prediction of glucose concentration at the chosen prediction horizon (PH) time. The method performance is assessed using datasets from two different CGM systems (nine subjects using the Medtronic [Northridge, CA] Guardian® and six subjects using the Abbott [Abbott Park, IL] Navigator®). Three different PHs are used: 15, 30, and 45 min. The NNM accuracy has been estimated by using the root mean square error (RMSE) and prediction delay. Results: The RMSE is around 10, 18, and 27 mg/dL for 15, 30, and 45 min of PH, respectively. The prediction delay is around 4, 9, and 14 min for upward trends and 5, 15, and 26 min for downward trends, respectively. A comparison with a previously published technique, based on an autoregressive model (ARM), has been performed. The comparison shows that the proposed NNM is more accurate than the ARM, with no significant deterioration in the prediction delay

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 220, June 1981

Approximately 137 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1981 are recorded, covering a variety of topics in aerospace medicine and biology