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.
General
| Imprint: |
Springer International Publishing AG
|
| Country of origin: |
Switzerland |
| Series: |
SpringerBriefs in Service Science |
| Release date: |
August 2023 |
| Authors: |
Hui Yang
• Bing Yao
|
| Dimensions: |
235 x 155mm (L x W) |
| Pages: |
88 |
| Edition: |
1st ed. 2023 |
| ISBN-13: |
978-3-03-135951-4 |
| Categories: |
Books
|
| LSN: |
3-03-135951-8 |
| Barcode: |
9783031359514 |
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