Oscar D Sanchez – författare

Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment focuses on bio-inspired techniques such as modelling 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 the 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 modelling of time series for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia using deep neural networks. The detection of diabetes mellitus in early stages or when current diagnostic techniques cannot detect glucose intolerance or prediabetes is proposed, carried out by means of deep neural networks in force in the literature. Readers will find leading-edge research in diabetes identification based on discrete high-order neural networks trained with the extended Kalman filter; parametric identification of compartmental models used to describe diabetes mellitus; modelling 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 modelling, 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.

Artificial Intelligence Innovations for Biomedical Engineering and Healthcare bridges the evolving domains of artificial intelligence and biomedical engineering and healthcare. In an era where data-driven insights and precision medicine are essential in healthcare, this book explores emerging trends and showcases AI's potential in transforming patient care, diagnosis, and the treatment of chronic diseases. It simplifies the relationship between artificial intelligence and biomedical engineering, elucidating how these technologies are revolutionizing self-care. The book goes on to examine how advanced technologies, including complex networks and AI-driven diagnostics are reshaping the healthcare landscape.From decoding complex networks to revealing AI's role in treating chronic diseases, this book serves as a guide to understanding how innovation is reshaping the healthcare landscape.Covers topics from basic to advanced applications, ensuring a holistic understanding of AI applications in biomedical engineering and healthcareProvides a thorough exploration of the integration of AI in biomedical engineering, addressing the information needs of professionals and researchersPresents real-world case studies and practical examples