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Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment
- 1st Edition - April 18, 2024
- Authors: Alma Y Alanis, Oscar D Sánchez, Alonso Vaca Gonzalez, Marco Perez Cisneros
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 2 3 4 1 - 9
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 2 3 4 0 - 2
Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment focuses on bioinspired techniques such as modeling to generate control algorithms for the… Read more
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Request a sales quoteBio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment focuses on bioinspired techniques such as modeling to generate control algorithms for the treatment of diabetes mellitus. The book addresses the identification of diabetes mellitus using a high-order recurrent neural network trained by an extended Kalman filter. The authors also describe the use of metaheuristic algorithms for the parametric identification of compartmental models of diabetes mellitus widely used in research works such as the Sorensen model and the Dallaman model. In addition, the book addresses the modeling of time series for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia using deep neural networks. The detection of diabetes mellitus in the early stages or when current diagnostic techniques cannot detect glucose intolerance or prediabetes is proposed, carried out by means of deep neural networks present in the literature. Readers will find leading-edge research in diabetes identification based on discrete high-order neural networks trained with an extended Kalman filter; parametric identification of compartmental models used to describe diabetes mellitus; modeling of data obtained by continuous glucose-monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia; and screening for glucose intolerance using glucose-tolerance test data and deep neural networks. Application of the proposed approaches is illustrated via simulation and real-time implementations for modeling, prediction, and classification.
- Addresses the online identification of diabetes mellitus using a high-order recurrent neural network trained online by an extended Kalman filter.
- Covers parametric identification of compartmental models used to describe diabetes mellitus.
- Provides modeling of data obtained by continuous glucose-monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia.
Researchers in computational modelling, applied mathematicians, and computer scientists working with researchers and developers in engineering, biomedical, and other applied sciences, research clinicians, biomedical engineers, and biomedical researchers with an interest in developing applied computational modelling for diabetes mellitus, researchers, developers, and graduate students in computer science, mathematics, and biomedical engineering interested in artificial neural networks and metaheuristic algorithms for mathematical modelling and treatment of diabetes mellitus
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- List of figures
- List of tables
- Biographies
- Alma Y. Alanis
- Oscar D. Sanchez
- Alonso Vaca-Gonzalez
- Marco A. Perez-Cisneros
- Preface
- 1: Introduction
- Abstract
- 1.1. Diabetes Mellitus from a medical point of view
- 1.2. Current diagnosis of Diabetes Mellitus and glucose intolerance
- 1.3. Current treatment of Diabetes Mellitus
- 1.4. Prediction of Diabetes Mellitus and its complications
- 1.5. Intelligent strategies inspired in biology and their applications in engineering and medicine
- References
- 2: Problem statement
- Abstract
- 2.1. State-of-the-art in modeling, identification, and detection of diabetes mellitus using bio-inspired strategies
- 2.2. Compartmental models Sorensen and Dalla Man
- 2.3. Serial data
- References
- 3: Mathematical preliminaries
- Abstract
- 3.1. Evolutionary algorithms
- 3.2. Particle swarm-based algorithms
- 3.3. Neural networks
- 3.4. Deep neural networks
- References
- 4: Parameter estimation for glucose–insulin dynamics
- Abstract
- 4.1. Affine system
- 4.2. Evolutionary optimization algorithms for parameter estimation
- 4.3. Parametric estimation results in compartmental models
- References
- 5: Neural model for glucose–insulin dynamics
- Abstract
- 5.1. Identification
- 5.2. Identification with artificial neural networks
- 5.3. System description
- References
- 6: Multistep predictor applied to T1DM patients
- Abstract
- 6.1. Prediction
- 6.2. Prediction results evaluation criteria
- 6.3. Results
- References
- 7: Classification and detection of diabetes mellitus and impaired glucose tolerance
- Abstract
- 7.1. Classification
- 7.2. K-means
- 7.3. Evaluation criteria
- 7.4. Results
- 7.5. Results discussion
- References
- 8: Conclusions and future work
- Abstract
- 8.1. Conclusions of Chapter 1
- 8.2. Conclusions of Chapter 2
- 8.3. Conclusions of Chapter 3
- 8.4. Conclusions of Chapter 4
- 8.5. Conclusions of Chapter 5
- 8.6. Conclusions of Chapter 6
- 8.7. Conclusions of Chapter 7
- 8.8. General conclusions
- A: Model parameters
- A.1. Dalla Man nominal parameter values
- A.2. Sorensen nominal parameter values
- Index
- No. of pages: 250
- Language: English
- Edition: 1
- Published: April 18, 2024
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9780443223419
- eBook ISBN: 9780443223402
AY
Alma Y Alanis
Dr. Alma Y. Alanis received her M.Sc. and Ph.D. degrees in electrical engineering from the Advanced Studies and Research Center of the National Polytechnic Institute (CINVESTAV-IPN), Guadalajara, Mexico. Since 2008 she has been with University of Guadalajara, where she is currently a Dean of the Technologies for Cyber-Human Interaction Division, CUCEI. She is also member of the Mexican National Research System (SNI-2) and member of the Mexican Academy of Sciences. She has published papers in recognized International Journals and Conferences, besides eight international books. Dr. Alanis is a Senior Member of the IEEE and Subject Editor of the Journal of Franklin Institute, Section Editor at Open Franklin, Technical Editor at ASME/IEEE Transactions on Mechatronics, and Associate Editor at IEEE Transactions on Cybernetics, Intelligent Automation & Soft Computing and Engeenering Applications of Artifical Intelligence. Moreover, Dr. Alanis is currently serving on a number of IEEE and IFAC Conference Organizing Committees. In 2013 Dr. Alanis received the grant for women in science by L'Oreal-UNESCO-AMC-CONACYT-CONALMEX. In 2015, she received the Marcos Moshinsky Research Award. Her research interest centers on artificial neural networks, learning systems, intelligent control, and intelligent systems.
Affiliations and expertise
Dean of Technologies for Cyber-Human Interaction Division (CUCEI), Universidad de Guadalajara, MexicoOD
Oscar D Sánchez
Dr. Oscar D. Sánchez received his Ph.D. degree in Electronic and Computer Science from the University of Guadalajara. He is a researcher in the Computer Department at the University Center for Exact Sciences and Engineering at the University of Guadalajara. Dr. Sánchez’s research interests are modeling and identification of systems, bioinformatics, optimization and prediction with intelligent systems.
Affiliations and expertise
Researcher, CUCEI, Universidad de Guadalajara, MexicoAV
Alonso Vaca Gonzalez
Dr. Alonso Vaca González is a physician at the University of Guadalajara. He holds a Master’s Degree in Medical Microbiology, with diplomas in Applied Public Health, Mental Health, Microbiota, Diagnostic Hematology, Occupational Health and Safety, and Health Business Administration. Dr. González was one of the founding members of the Program for Early Detection and Counseling in Sexually Transmitted Infections (PRODOCITS), and is the author of indexed articles, manuals, and book chapters on the subject of microbiology. He has also advised the preparation of theses at the postgraduate level. Dr. González received the "Irene Robledo García" Award for Social Service in 2022. Dr. González carried out research activities at the Specialized Center for Reproduction and Genetics (CERGEN) and at Summa Corporation, evaluating products and possible treatments for people with Diabetes Mellitus. Dr. González is a professor at the Institutional System for Safety, Health and Environment (SISSMA), where he teaches courses on human capital training and skills assessment.
Affiliations and expertise
Physician, CUCEI, Universidad de Guadalajara, MexicoMP
Marco Perez Cisneros
Dr. Marco Perez-Cisneros received his M.Eng. degree from ITESO University, Mexico, and his Ph.D. degree from The University of Manchester, U.K. He currently works as a Professor with the Electro-Photonics Department at University of Guadalajara and has been appointed as the Rector of the University Centre of Exact Sciences and Engineering of the University of Guadalajara. Dr. Perez-Cisneros is a member of the National Research System in Mexico and is a member of the Mexican National Science Academy.
Affiliations and expertise
Rector, University Centre of Exact Sciences and Engineering, Universidad de Guadalajara, MexicoRead Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment on ScienceDirect