scholarly journals The mathematician's control toolbox for management of type 1 diabetes

2014 ◽  
Vol 4 (5) ◽  
pp. 20140042 ◽  
Author(s):  
Marie Csete ◽  
John Doyle
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Feedback Loop ◽  
Artificial Pancreas ◽  
Glucagon Release ◽  
Control Engineering ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Physiological Feedback

Blood glucose levels are controlled by well-known physiological feedback loops: high glucose levels promote insulin release from the pancreas, which in turn stimulates cellular glucose uptake. Low blood glucose levels promote pancreatic glucagon release, stimulating glycogen breakdown to glucose in the liver. In healthy people, this control system is remarkably good at maintaining blood glucose in a tight range despite many perturbations to the system imposed by diet and fasting, exercise, medications and other stressors. Type 1 diabetes mellitus (T1DM) results from loss of the insulin-producing cells of the pancreas, the beta cells. These cells serve as both sensor (of glucose levels) and actuator (insulin/glucagon release) in a control physiological feedback loop. Although the idea of rebuilding this feedback loop seems intuitively easy, considerable control mathematics involving multiple types of control schema were necessary to develop an artificial pancreas that still does not function as well as evolved control mechanisms. Here, we highlight some tools from control engineering used to mimic normal glucose control in an artificial pancreas, and the constraints, trade-offs and clinical consequences inherent in various types of control schemes. T1DM can be viewed as a loss of normal physiologic controls, as can many other disease states. For this reason, we introduce basic concepts of control engineering applicable to understanding pathophysiology of disease and development of physiologically based control strategies for treatment.

scholarly journals LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3303
Author(s):  
Jeremy Beauchamp ◽  
Razvan Bunescu ◽  
Cindy Marling ◽  
Zhongen Li ◽  
Chang Liu
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Diabetes Management ◽  
Artificial Pancreas ◽  
Real Patient ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Novel Approach ◽  
The 1960S

To avoid serious diabetic complications, people with type 1 diabetes must keep their blood glucose levels (BGLs) as close to normal as possible. Insulin dosages and carbohydrate consumption are important considerations in managing BGLs. Since the 1960s, models have been developed to forecast blood glucose levels based on the history of BGLs, insulin dosages, carbohydrate intake, and other physiological and lifestyle factors. Such predictions can be used to alert people of impending unsafe BGLs or to control insulin flow in an artificial pancreas. In past work, we have introduced an LSTM-based approach to blood glucose level prediction aimed at “what-if” scenarios, in which people could enter foods they might eat or insulin amounts they might take and then see the effect on future BGLs. In this work, we invert the “what-if” scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future. Leveraging a recent state-of-the-art model for time series forecasting, we then derive a novel architecture for the same recommendation task, in which the two LSTM chain is used as a repeating block inside a deep residual architecture. Experimental evaluations using real patient data from the OhioT1DM dataset show that the new integrated architecture compares favorably with the previous LSTM-based approach, substantially outperforming the baselines. The promising results suggest that this novel approach could potentially be of practical use to people with type 1 diabetes for self-management of BGLs.

776-P: Characteristics of Overnight Blood Glucose Levels Are Related to Sleep Quality in People with Type 1 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 776-P
Author(s):  
RACHEL BRANDT ◽  
MINSUN PARK ◽  
LAURIE T. QUINN ◽  
MINSEUNG CHU ◽  
YOUNGKWAN SONG ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Sleep Quality ◽  
Glucose Levels ◽  
Blood Glucose Levels

A Review of Type 1 Diabetes (T1D)

2020 ◽  
pp. 13-49
Author(s):  
Eileen O'Donnell ◽  
Liam O'Donnell
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Acting Insulin ◽  
Short Period ◽  
Sporting Activities ◽  
To Come

The diagnosis of Type 1 Diabetes (T1D) will come as an unwelcome surprise to most people. Within a short period of time, the person will have to come to understand and manage this chronic illness. The terminology associated with the T1D condition will also be totally new to the person: diabetes mellitus, pancreas, hyperglycaemia (hyper), hypoglycaemia (hypo), bolus (fast acting insulin), basal (slow acting insulin), ketones and blood glucose levels. The purpose of this article is to assist newly diagnosed patients' understanding of T1D, people who are already living with T1D, carers of people with T1D, partners and family members of someone with T1D, work colleagues, and friends who participate in the same sporting activities or go on holiday with a person who has T1D. In addition, this article reviews how people living with T1D can still enjoy exercise and maintain the best quality of life possible; whilst controlling the blood glucose levels in their body for the rest of their lives to prevent the onset of complications associated with diabetes.

scholarly journals Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Arthur Bertachi ◽  
Lyvia Biagi ◽  
Aleix Beneyto ◽  
Josep Vehí
Keyword(s):  
Type 1 Diabetes ◽  
Insulin Infusion ◽  
Artificial Pancreas ◽  
The Body ◽  
Control Algorithms ◽  
Rule Based ◽  
Control Blood Glucose ◽  
Glucose Levels ◽  
Blood Glucose Levels

The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients’ safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients’ safety, which reveals the feasibility of the approach to be included in different control algorithms.

Effect of delay on measurement of blood glucose levels in young subjects with type 1 diabetes

2009 ◽  
Vol 86 (2) ◽  
pp. e31-e33 ◽  
Author(s):  
Daniela Elleri ◽  
Carlo L. Acerini ◽  
Janet M. Allen ◽  
Anne-Mette F. Larsen ◽  
Malgorzata E. Wilinska ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Young Subjects
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