Modeling and Prediction in Diabetes Physiology

Diabetes is a group of metabolic diseases characterized by the inability of the organism to autonomously regulate the blood glucose levels. It requires continuing medical care to prevent acute complications and to reduce the risk of long-term complications. Inadequate glucose control is associated with damage, dysfunction and failure of various organs. The management of the disease is non trivial and demanding. With today’s standards of current diabetes care, good glucose regulation needs constant attention and decision-making by the individuals with diabetes. Empowering the patients with a decision support system would, therefore, improve their quality of life without additional burdens nor replacing human expertise. This thesis investigates the use of data-driven techniques to the purpose of glucose metabolism modeling and short-term blood-glucose predictions in Type I Diabetes Mellitus (T1DM). The goal was to use models and predictors in an advisory tool able to produce personalized short-term blood glucose predictions and on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise, to help diabetic subjects maintaining glycemia as close to normal as possible. The approaches taken to describe the glucose metabolism were discrete-time and continuous-time models on input-output form and statespace form, while the blood glucose short-term predictors, i.e., up to 120 minutes ahead, used ARX-, ARMAX- and subspace-based prediction

Model Identification from Ambulatory Data for Post-Prandial Glucose Control in type 1 Diabetes

Several glucoregulatory models are studies and a new model is proposed. Experiments are developed following an optimal design methodology. The designed experiments are applied in home monitoring of diabetic patients.Laguna Sanz, AJ. (2010). Model Identification from Ambulatory Data for Post-Prandial Glucose Control in type 1 Diabetes. http://hdl.handle.net/10251/14052Archivo delegad

Online Glucose Prediction in Type-1 Diabetes by Neural Network Models

Diabetes mellitus is a chronic disease characterized by dysfunctions of the normal regulation of glucose concentration in the blood. In Type 1 diabetes the pancreas is unable to produce insulin, while in Type 2 diabetes derangements in insulin secretion and action occur. As a consequence, glucose concentration often exceeds the normal range (70-180 mg/dL), with short- and long-term complications. Hypoglycemia (glycemia below 70 mg/dL) can progress from measurable cognition impairment to aberrant behaviour, seizure and coma. Hyperglycemia (glycemia above 180 mg/dL) predisposes to invalidating pathologies, such as neuropathy, nephropathy, retinopathy and diabetic foot ulcers. Conventional diabetes therapy aims at maintaining glycemia in the normal range by tuning diet, insulin infusion and physical activity on the basis of 4-5 daily self-monitoring of blood glucose (SMBG) measurements, obtained by the patient using portable minimally-invasive lancing sensor devices. New scenarios in diabetes treatment have been opened in the last 15 years, when minimally invasive continuous glucose monitoring (CGM) sensors, able to monitor glucose concentration in the subcutis continuously (i.e. with a reading every 1 to 5 min) over several days (7-10 consecutive days), entered clinical research. CGM allows tracking glucose dynamics much more effectively than SMBG and glycemic time-series can be used both retrospectively, e.g. to optimize metabolic control therapy, and in real-time applications, e.g. to generate alerts when glucose concentration exceeds the normal range thresholds or in the so-called “artificial pancreas”, as inputs of the closed loop control algorithm. For what concerns real time applications, the possibility of preventing critical events is, clearly, even more appealing than just detecting them as they occur. This would be doable if glucose concentration were known in advance, approximately 30-45 min ahead in time. The quasi continuous nature of the CGM signal renders feasible the use of prediction algorithms which could allow the patient to take therapeutic decisions on the basis of future instead of current glycemia, possibly mitigating/ avoiding imminent critical events. Since the introduction of CGM devices, various methods for short-time prediction of glucose concentration have been proposed in the literature. They are mainly based on black box time series models and the majority of them uses only the history of the CGM signal as input. However, glucose dynamics are influenced by many factors, e.g. quantity of ingested carbohydrates, administration of drugs including insulin, physical activity, stress, emotions and inter- and intra-individual variability is high. For these reasons, prediction of glucose time course is a challenging topic and results obtained so far may be improved. The aim of this thesis is to investigate the possibility of predicting future glucose concentration, in the short term, using new models based on neural networks (NN) exploiting, apart from CGM history, other available information. In particular, we first develop an original model which uses, as inputs, the CGM signal and information on timing and carbohydrate content of ingested meals. The prediction algorithm is based on a feedforward NN in parallel with a linear predictor. Results are promising: the predictor outperforms widely used state of art techniques and forecasts are accurate and allow obtaining a satisfactory time anticipation. Then we propose a second model, which exploits a different NN architecture, a jump NN, which combines benefits of both feedforward NN and linear algorithm obtaining performance similar to the previously developed predictor, although the simpler structure. To conclude the analysis, information on doses of injected bolus of insulin are added as input of the jump NN and the relative importance of every input signal in determining the NN output is investigated by developing an original sensitivity analysis. All the proposed predictors are assessed on real data of Type 1 diabetics, collected during the European FP7 project DIAdvisor. To evaluate the clinical usefulness of prediction in improving diabetes management we also propose a new strategy to quantify, using an in silico environment, the reduction of hypoglycemia when alerts and relative therapy are triggered on the basis of prediction, obtained with our NN algorithm, instead of CGM. Finally, possible inclusion of additional pieces of information such as physical activity is investigated, though at a preliminary level. The thesis is organized as follows. Chapter 1 gives an introduction to the diabetes disease and the current technologies for CGM, presents state of art techniques for short-time prediction of glucose concentration of diabetics and states the aim and the novelty of the thesis. Chapter 2 discusses NN paradigms from a theoretical point of view and specifies technical details common to the design and implementation of all the NN algorithms proposed in the following. Chapter 3 describes the first prediction model we propose, based on a NN in parallel with a linear algorithm. Chapter 4 presents an alternative simpler architecture, based on a jump NN, and demonstrates its equivalence, in terms of performance, with the previously proposed algorithm. Chapter 5 further improves the jump NN, by adding new inputs and investigating their effective utility by a sensitivity analysis. Chapter 6 points out possible future developments, as the possibility of exploiting information on physical activity, reporting also a preliminary analysis. Finally, Chapter 7 describes the application of NN for generation of preventive hypoglycemic alerts and evaluates improvement of diabetes management in a simulated environment. Some concluding remarks end the thesis

Neural Simplex Architecture

We present the Neural Simplex Architecture (NSA), a new approach to runtime assurance that provides safety guarantees for neural controllers (obtained e.g. using reinforcement learning) of autonomous and other complex systems without unduly sacrificing performance. NSA is inspired by the Simplex control architecture of Sha et al., but with some significant differences. In the traditional approach, the advanced controller (AC) is treated as a black box; when the decision module switches control to the baseline controller (BC), the BC remains in control forever. There is relatively little work on switching control back to the AC, and there are no techniques for correcting the AC's behavior after it generates a potentially unsafe control input that causes a failover to the BC. Our NSA addresses both of these limitations. NSA not only provides safety assurances in the presence of a possibly unsafe neural controller, but can also improve the safety of such a controller in an online setting via retraining, without overly degrading its performance. To demonstrate NSA's benefits, we have conducted several significant case studies in the continuous control domain. These include a target-seeking ground rover navigating an obstacle field, and a neural controller for an artificial pancreas system.Comment: 12th NASA Formal Methods Symposium (NFM 2020

Improving management of type 1 diabetes in the UK: the Dose Adjustment For Normal Eating (DAFNE) programme as a research test-bed. A mixed-method analysis of the barriers to and facilitators of successful diabetes self-management, a health economic analysis, a cluster randomised controlled trial of different models of delivery of an educational intervention and the potential of insulin pumps and additional educator input to improve outcomes

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