Overview
- Focuses on applications for optimizing medical decision making in spatiotemporal cardiovascular disease processes
- Helps readers understand emerging topics in cardiac computational modeling and optimization
- Highlights research advances at the interface of engineering, medical and information sciences to treat heart disease
Part of the book series: SpringerBriefs in Service Science (SSS)
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Table of contents (5 chapters)
Keywords
- Computer Simulation
- Active Learning
- Medical Decision Making
- Cardiac Modeling
- Sensor-based Learning
- Simulation Optimization
- Machine Learning
- Sequential Optimization
- Heart Disease
- Disease Diagnostics and Prognostics
- Physiological Signal Processing
- Cardiovascular Systems
- Diagnosis and Surgical Planning
About this book
This book reviews the development of physics-based modeling and sensor-based data fusion for optimizing medical decision making in connection with spatiotemporal cardiovascular disease processes. To improve cardiac care services and patients’ quality of life, it is very important to detect heart diseases early and optimize medical decision making. This book introduces recent research advances in machine learning, physics-based modeling, and simulation optimization to fully exploit medical data and promote the data-driven and simulation-guided diagnosis and treatment of heart disease. Specifically, it focuses on three major topics: computer modeling of cardiovascular systems, physiological signal processing for disease diagnostics and prognostics, and simulation optimization in medical decision making. It provides a comprehensive overview of recent advances in personalized cardiac modeling by integrating physics-based knowledge of the cardiovascular system with machine learning and multi-source medical data. It also discusses the state-of-the-art in electrocardiogram (ECG) signal processing for the identification of disease-altered cardiac dynamics. Lastly, it introduces readers to the early steps of optimal decision making based on the integration of sensor-based learning and simulation optimization in the context of cardiac surgeries.
This book will be of interest to researchers and scholars in the fields of biomedical engineering, systems engineering and operations research, as well as professionals working in the medical sciences.
Authors and Affiliations
About the authors
Dr. Hui Yang is an IISE fellow, a Professor of Industrial & Manufacturing Engineering and Biomedical Engineering at Pennsylvania State University, USA, and is affiliated with the Penn State Cancer Institute (PSCI), Clinical and Translational Science Institute (CTSI), Institute for Computational and Data Sciences (ICDS), and CIMP-3D. He is a department editor of IISE Transactions on Healthcare Systems Engineering; associate editor of IISE Transactions, IEEE Journal of Biomedical & Health Informatics, IEEE Transactions on Automation Science & Engineering, and IEEE Robotics & Automation Letters; and Associate Editor for Proceedings of IEEE CASE, EMBC, and BHI. Dr. Yang is specialized in physics-based and data-driven models of cardiac systems – from ion channels to cells to tissues to the entire heart – for optimized medical decision making.
Dr. Bing Yao is an Assistant Professor at the Department of Industrial and Systems Engineering, University of Tennessee Knoxville, USA. Before joining UTK, she was an Assistant Professor at the School of Industrial Engineering and Management, Oklahoma State University, USA. She received her Ph.D. in Industrial Engineering and Operations Research from Pennsylvania State University. Her research interests include biomedical and health informatics, computer simulation and optimization, data mining and signal processing, and physical-statistical modeling.
Bibliographic Information
Book Title: Sensing, Modeling and Optimization of Cardiac Systems
Book Subtitle: A New Generation of Digital Twin for Heart Health Informatics
Authors: Hui Yang, Bing Yao
Series Title: SpringerBriefs in Service Science
DOI: https://doi.org/10.1007/978-3-031-35952-1
Publisher: Springer Cham
eBook Packages: Business and Management, Business and Management (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Softcover ISBN: 978-3-031-35951-4Published: 19 August 2023
eBook ISBN: 978-3-031-35952-1Published: 18 August 2023
Series ISSN: 2731-3743
Series E-ISSN: 2731-3751
Edition Number: 1
Number of Pages: X, 88
Number of Illustrations: 1 b/w illustrations, 37 illustrations in colour
Topics: Operations Management, Health Care Management, Operations Research/Decision Theory, Machine Learning, Optimization, Biomedical Engineering and Bioengineering