Managing Blood Glucose Concentration in Type 1 Diabetes with Deep Learning-Based Methodologies
| dc.contributor.advisor | Cescon, Marzia | |
| dc.contributor.committeeMember | Grigoriadis, Karolos M. | |
| dc.contributor.committeeMember | Song, Gangbing | |
| dc.contributor.committeeMember | Chen, Zheng | |
| dc.contributor.committeeMember | Prasad, Saurabh | |
| dc.creator | Jaloli, Mehrad | |
| dc.creator.orcid | 0000-0003-2417-9263 | |
| dc.date.accessioned | 2024-01-24T15:28:21Z | |
| dc.date.created | August 2023 | |
| dc.date.issued | 2023-08 | |
| dc.date.updated | 2024-01-24T15:28:22Z | |
| dc.description.abstract | This thesis presents groundbreaking research on advanced models and automated systems designed to revolutionize Blood Glucose (BG) management for individuals with type 1 diabetes (T1D). Integrating cutting-edge deep learning algorithms, phys- iological modeling, and RL techniques, the primary objective is to improve diabetes care, optimize glucose control, and enhance the overall quality of life for T1D patients. The journey commences with the development of state-of-the-art BG predictive mod- els, proposing a powerful deep learning-based model to achieve remarkable accuracy in glucose predictions. Additionally, the impact of behavioral factors, such as physical activity and stress, on BG fluctuations, is explored, further enhancing model accuracy and generalizability. Furthermore, innovative BG testing platforms are introduced, including a data- driven model predictive control algorithm integrated with a BG predictor, showcasing superior glucose control compared to traditional linear control models, and deriving BG dynamic models in response to physical activity, providing invaluable insights for the design of automated closed-loop systems aimed at improved glucose control in daily life. The final frontier lies in RL-based automated insulin delivery systems, with two pivotal papers presenting closed-loop insulin administration frameworks dynamically adjusting insulin dosages based on real-time glucose readings and meal intakes, show- casing significant reductions in glucose variability and improvements in time spent within the target glucose range. In conclusion, this comprehensive thesis showcases innovative models and systems to elevate BG management in T1Ds, offering hope for a brighter future for those living with this condition as advancements continue to reshape diabetes care through personalized and automated approaches. | |
| dc.description.department | Mechanical Engineering, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Portions of this document appear in: Jaloli, Mehrad, and Marzia Cescon. "Long-term prediction of blood glucose levels in type 1 diabetes using a cnn-lstm-based deep neural network." Journal of Diabetes Science and Technology 17, no. 6 (2023): 1590-1601; and in: Jaloli, Mehrad, William Lipscomb, and Marzia Cescon. "Incorporating the Effect of Behavioral States in Multi-Step Ahead Deep Learning Based Multivariate Predictors for Blood Glucose Forecasting in Type 1 Diabetes." BioMedInformatics 2, no. 4 (2022): 715-726; and in: Cescon, Marzia, and Mehrad Jaloli. "System and method for predicting blood-glucose concentration." U.S. Patent Application 17/889,611, filed February 23, 2023; and in: Aiello, Eleonora Maria, Mehrad Jaloli, and Marzia Cescon. "Model Predictive Control (MPC) of an artificial pancreas with data-driven learning of multi-step-ahead blood glucose predictors." Control Engineering Practice (2023): 105810; and in: Jaloli, Mehrad, and Marzia Cescon. "Modeling Physical Activity Impact on Glucose Dynamics in People with Type 1 Diabetes for a Fully Automated Artificial Pancreas." In 2023 IEEE Conference on Control Technology and Applications (CCTA), pp. 546-551. IEEE, 2023; and in: Jaloli, Mehrad, and Marzia Cescon. "Reinforcement Learning for Multiple Daily Injection (MDI) Therapy in Type 1 Diabetes (T1D)." BioMedInformatics 3, no. 2 (2023): 422-433; and in: Jaloli, Mehrad, and Marzia Cescon. "Basal-bolus advisor for type 1 diabetes (T1D) patients using multi-agent reinforcement learning (RL) methodology." Control Engineering Practice 142 (2024): 105762; and in: Jaloli, Mehrad, and Marzia Cescon. "Predicting Blood Glucose Levels Using CNN-LSTM Neural Networks." In Diabetes Technology Meeting. 2020; and in: Jaloli, M., and M. Cescon. "DEMONSTRATING THE EFFECT OF DAILY STRESS ON BLOOD GLUCOSE LEVEL VARIATION IN TYPE 1 DIABETES." In DIABETES TECHNOLOGY & THERAPEUTICS, vol. 24, pp. A234-A234. 140 HUGUENOT STREET, 3RD FL, NEW ROCHELLE, NY 10801 USA: MARY ANN LIEBERT, INC, 2022; and in: Jaloli, Mehrad., and Cescon, Marzia "Demonstrating the Effect of Daily Physical Activities on Blood Glucose Level Variation in Type 1 Diabetes." In DIABETES TECHNOLOGY & THERAPEUTICS, vol. 24, pp. A79-A79. 140 HUGUENOT STREET, 3RD FL, NEW ROCHELLE, NY 10801 USA: MARY ANN LIEBERT, INC, 2022.. | |
| dc.identifier.uri | https://hdl.handle.net/10657/16005 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Blood Glucose Management, Type 1 Diabetes, Decision Support Systems, Artificial Pancreas, Data-Driven Learning | |
| dc.title | Managing Blood Glucose Concentration in Type 1 Diabetes with Deep Learning-Based Methodologies | |
| dc.type.dcmi | text | |
| dc.type.genre | Thesis | |
| dcterms.accessRights | The full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period. | |
| local.embargo.lift | 2025-08-01 | |
| local.embargo.terms | 2025-08-01 | |
| thesis.degree.college | Cullen College of Engineering | |
| thesis.degree.department | Mechanical Engineering, Department of | |
| thesis.degree.discipline | Mechanical Engineering | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |