Overview
- Covers each designed strategy which can effectively reduce the number of tumor cells with a minimal dose of drugs
- Develops various mathematical models of tumor cell and immune cell based on existing medical studies
- Introduces adaptive dynamic programming into the solution of optimal control strategies
- Is open access, which means that you have free and unlimited access
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Table of contents (7 chapters)
Authors and Affiliations
About the authors
Dr. Sun is a member of the Institute of Electrical and Electronics Engineers (IEEE), the Chinese Association of Automation (CAA) and Chinese Association for Artificial Intelligence (CAAI), where she works as the Intelligent Adaptive Cooperative Optimization Control Committee’s Vice Secretary-General.
Shun Xu received the B.S. degree in clinical medicine, the M.S. degree in surgery, and the Ph.D. degree in thoracic surgery from China Medical University, Shenyang, China, in 1987, 1990, and 1995, respectively. He studied in Japan from 1994 to 1996. He is a chief physician, director of thoracic surgery, and director of the Lung Cancer Research Laboratory at China Medical University's Cancer Institute. He is also a national second-level professor and a doctoral supervisor. He has presided over and participated in a number of national, provincial, and ministerial level research works. His research interests include applications of reinforcement learning, convolution neural network, and pattern recognition in medical image, especially for diagnosis of lung cancer, esophageal cancer, and other thoracic tumors.
Dr. Xu has been awarded the State Council Special Allowance from the State Council. He is a member of the ThoracicCardiovascular Surgery Branch Committee of the Chinese Medical Association. He serves as the chairman of the Thoracic Surgery Branch Committee of the Liaoning Medical Association.
Yang Liu is a medical doctor specializing in thoracic surgery. He holds the positions of associate professor, associate chief physician, and master's supervisor for graduate students. He has authored numerous articles in both national and international medical journals, focusing on the topics of lung cancer and tumors. His research interests include reinforcement learning, adaptive dynamic programming, neural networks, and their applications in diagnosing lung diseases.
Dr. Liu is a member of the Cell Biology Cardiopulmonary Rehabilitation Committee in Liaoning Province. His primary research area revolves around standardized treatment for lung cancer and mediastinal tumors. He has led a youth fund of the National Natural Science Foundation of China, and has also participated in the National Natural Science Foundation of China (General Project). Additionally, he serves as an expert for the National Natural Science Foundation Committee.
Huaguang Zhang received the B.S. and M.S. degrees in control engineering from the Northeast Dianli University of China, Jilin City, China, in 1982 and 1985, respectively, and the Ph.D. degree in thermal power engineering and automation from Southeast University, Nanjing, China, in 1991. He joined the Department of Automatic Control, Northeastern University, Shenyang, China, in 1992, as a Postdoctoral Fellow, for two years, where he has been a Professor and the Head of the Institute of Electric Automation, College of Information Science and Engineering since 1994. He has authored or coauthored over 200 journal and conference papers and four monographs and has co-invented 20 patents. His current research interests include fuzzy control, stochastic-system control, neural-network-based control, nonlinear control, and their applications.
Bibliographic Information
Book Title: Adaptive Dynamic Programming
Book Subtitle: For Chemotherapy Drug Delivery
Authors: Jiayue Sun, Shun Xu, Yang Liu, Huaguang Zhang
DOI: https://doi.org/10.1007/978-981-99-5929-7
Publisher: Springer Singapore
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s) 2024
Hardcover ISBN: 978-981-99-5928-0Published: 13 September 2023
Softcover ISBN: 978-981-99-5931-0Published: 13 September 2023
eBook ISBN: 978-981-99-5929-7Published: 12 September 2023
Edition Number: 1
Number of Pages: XV, 135
Number of Illustrations: 2 b/w illustrations, 38 illustrations in colour