A Nonlinear Data-Driven Model of Glucose Dynamics Accounting for Physical Activity for Type 1 Diabetes: An In Silico Study

2016 ◽  
Author(s):  
Jinyu Xie ◽  
Qian Wang
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Clinical Training ◽  
Time Series Data ◽  
Artificial Pancreas ◽  
Data Driven ◽  
Series Data ◽  
Data Sets ◽  
Using Data

Physical activity is an important physiological information which should be taken into account by artificial pancreas to achieve optimal control of blood glucose in Type 1 Diabetes patients. An accurate glucose dynamic model with physical activity as an additional input is highly desirable for the next generation artificial pancreas. In this paper, we present a nonlinear data-driven model that captures both the insulin-independent and -dependent effect of physical activity, especially the prolonged effect of physical activity on insulin sensitivity that can last 24–48 hours post exercise. The model was identified and validated using data sets generated by a physiological glucose-exercise model under a clinical training protocol. Compared to modeling the effect of physical activity as a linear additive term only in a glucose dynamic equation, the proposed nonlinear model showed significant improvement of prediction accuracy in all three metrics, particularly in large prediction horizons (P < 0.05). Further investigation in time-series data indicates that the improvement mainly resulted from the better prediction of glucose around the first meal time after exercise (6 to 8 hours after the meal was taken).

scholarly journals Improving Glucose Prediction Accuracy in Physically Active Adolescents With Type 1 Diabetes

2019 ◽  
Vol 13 (4) ◽  
pp. 718-727 ◽  
Author(s):  
Nicole Hobbs ◽  
Iman Hajizadeh ◽  
Mudassir Rashid ◽  
Kamuran Turksoy ◽  
Marc Breton ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Type 1 Diabetes ◽  
Prediction Accuracy ◽  
Artificial Pancreas ◽  
Metabolic State ◽  
Physiological Variables ◽  
Additional Input ◽  
Glucose Prediction

Background: Physical activity presents a significant challenge for glycemic control in individuals with type 1 diabetes. As accurate glycemic predictions are key to successful automated decision-making systems (eg, artificial pancreas, AP), the inclusion of additional physiological variables in the estimation of the metabolic state may improve the glucose prediction accuracy during exercise. Methods: Predictor-based subspace identification is applied to a dynamic glucose prediction model including heart rate measurements along with variables representing the carbohydrate consumption and insulin boluses. To demonstrate the improvement in prediction ability due to the additional heart rate variable, the performance of the proposed modeling technique is evaluated with (SID-HR) and without heart rate (SID-2) as an additional input using experimental data involving adolescents at ski camp. Furthermore, the performance of the proposed approach is compared to that of the metabolic state observer (MSO) model currently used in the University of Virginia AP algorithm. Results: The addition of heart rate in the subspace-based model (SID-HR) yields a statistically significant improvement in the root-mean-square error compared to the SID-2 model ( P < .001) and the standard MSO ( P < .001). Furthermore, the SID-HR model performed favorably in comparison to the SID-2 and MSO models after accounting for its increased complexity. Conclusions: Directly considering the effects of physical activity levels on glycemic dynamics through the inclusion of heart rate as an additional input variable in the glucose dynamics model improves the glucose prediction accuracy. The proposed methodology could improve exercise-informed model-based predictive control algorithms in artificial pancreas systems.

scholarly journals Location Prediction Models using Data Mining and Machine Learning

2020 ◽  
Vol 9 (3) ◽  
pp. 3383-3390
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Time Series Data ◽  
Real Life ◽  
Series Data ◽  
Data Sets ◽  
Location Prediction ◽  
Summary Table ◽  
User Data ◽  
Using Data

A vast availability of location based user data which is generated everyday whether it is GPS data from online cabs, or weather time series data, is essential in many ways to the user and has been applied to many real life applications such as location targeted-advertising, recommendation systems, crime-rate detection, home trajectory analysis etc. In order to analyze this data and use it to fruitfulness a vast majority of prediction models have been proposed and utilized over the years. A next location prediction model is a model that uses this data and can be designed as a combination of two or more models and techniques, but these have their own pros and cons. The aim of this document is to analyze and compare the various machine learning models and related experiments that can be applied for better location prediction algorithms in the near future. The paper is organized in a way so as to give readers insights and other noteworthy points and inferences from the papers surveyed. A summary table has been presented to get a glimpse of the methods in depth and our added inferences along with the data-sets analyzed.

scholarly journals Artificial Pancreas Systems and Physical Activity in Patients with Type 1 Diabetes: Challenges, Adopted Approaches, and Future Perspectives

2019 ◽  
Vol 13 (6) ◽  
pp. 1077-1090 ◽  
Author(s):  
Sémah Tagougui ◽  
Nadine Taleb ◽  
Joséphine Molvau ◽  
Élisabeth Nguyen ◽  
Marie Raffray ◽  
...  
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Healthcare Professionals ◽  
Therapeutic Option ◽  
Artificial Pancreas ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Delivery Methods ◽  
Current State

Physical activity is important for patients living with type 1 diabetes (T1D) but limited by the challenges associated with physical activity induced glucose variability. Optimizing glycemic control without increasing the risk of hypoglycemia is still a hurdle despite many advances in insulin formulations, delivery methods, and continuous glucose monitoring systems. In this respect, the artificial pancreas (AP) system is a promising therapeutic option for a safer practice of physical activity in the context of T1D. It is important that healthcare professionals as well as patients acquire the necessary knowledge about how the AP system works, its limits, and how glucose control is regulated during physical activity. This review aims to examine the current state of knowledge on exercise-related glucose variations especially hypoglycemic risk in T1D and to discuss their effects on the use and development of AP systems. Though effective and highly promising, these systems warrant further research for an optimized use around exercise.

scholarly journals Continuous Glucose Monitoring and Exercise in Type 1 Diabetes: Past, Present and Future

Biosensors ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 73 ◽  
Author(s):  
Shaelyn Houlder ◽  
Jane Yardley
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Continuous Glucose Monitoring ◽  
Closed Loop ◽  
Artificial Pancreas ◽  
Glucose Monitoring ◽  
Post Exercise ◽  
Exercise Period ◽  
Academic Search

Prior to the widespread use of continuous glucose monitoring (CGM), knowledge of the effects of exercise in type 1 diabetes (T1D) was limited to the exercise period, with few studies having the budget or capacity to monitor participants overnight. Recently, CGM has become a staple of many exercise studies, allowing researchers to observe the otherwise elusive late post-exercise period. We performed a strategic search using PubMed and Academic Search Complete. Studies were included if they involved adults with T1D performing exercise or physical activity, had a sample size greater than 5, and involved the use of CGM. Upon completion of the search protocol, 26 articles were reviewed for inclusion. While outcomes have been variable, CGM use in exercise studies has allowed the assessment of post-exercise (especially nocturnal) trends for different exercise modalities in individuals with T1D. Sensor accuracy is currently considered adequate for exercise, which has been crucial to developing closed-loop and artificial pancreas systems. Until these systems are perfected, CGM continues to provide information about late post-exercise responses, to assist T1D patients in managing their glucose, and to be useful as a tool for teaching individuals with T1D about exercise.

89-LB: The Effect of GIP on Plasma Glucose in a Setting of Prandial Insulin Overdose and Physical Activity after Meal Intake in Patients with Type 1 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 89-LB ◽  
Author(s):  
BJØRN HOE ◽  
SEBASTIAN M. NGUYEN HEIMBÜRGER ◽  
LÆRKE S. GASBJERG ◽  
MADS B. LYNGGAARD ◽  
BOLETTE HARTMANN ◽  
...  
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Plasma Glucose ◽  
Prandial Insulin ◽  
Meal Intake ◽  
Insulin Overdose

scholarly journals Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 540
Author(s):  
Fabio Amaral ◽  
Wallace Casaca ◽  
Cassio M. Oishi ◽  
José A. Cuminato
Keyword(s):  
Learning Strategies ◽  
Time Series Data ◽  
Sao Paulo ◽  
The State ◽  
Data Driven ◽  
Series Data ◽  
São Paulo ◽  
Time Data ◽  
Unified Framework ◽  
Infected People

São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given.

A Qualitative Study of the Views of Individuals With Type 1 Diabetes on the Ethical Considerations Raised by the Artificial Pancreas

2020 ◽  
Vol 10 (3) ◽  
pp. 237-261
Author(s):  
Ariane Quintal ◽  
Virginie Messier ◽  
Rémi Rabasa-Lhoret ◽  
Eric Racine
Keyword(s):  
Type 1 Diabetes ◽  
Qualitative Study ◽  
Artificial Pancreas ◽  
Ethical Considerations

Dual‐hormone artificial pancreas for management of type 1 diabetes: Recent progress and future directions

2021 ◽  
Author(s):  
Marco Infante ◽  
David A. Baidal ◽  
Michael R. Rickels ◽  
Andrea Fabbri ◽  
Jay S. Skyler ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Recent Progress ◽  
Artificial Pancreas ◽  
Future Directions
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