Deep Learning Techniques for Biomedical and Health Informatics
Published on 14. January 2020
Book
Paperback/Softback
367 pages
978-0-12-819061-6 (ISBN)
Description
Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Biomedical engineers, researchers in data analytics, Big Data, health care management and intelligent systems.
Dimensions
Height: 235 mm
Width: 191 mm
Weight
750 gr
ISBN-13
978-0-12-819061-6 (9780128190616)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Basant Agarwal | Valentina Emilia Balas | Lakhmi C. Jain
Deep Learning Techniques for Biomedical and Health Informatics
E-Book
01/2020
Academic Press
€131.00
Available for download
Persons
Dr. Basant Agarwal works as an Assistant Professor at the Indian Institute of Information Technology Kota (IIIT-Kota), India, which is an Institute of National Importance. He holds a Ph.D. and M.Tech. from the Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, India. He has more than 9 years of experience in research and teaching. He has worked as a Postdoc Research Fellow at the Norwegian University of Science and Technology (NTNU), Norway, under the prestigious ERCIM (European Research Consortium for Informatics and Mathematics) fellowship in 2016. He has also worked as a Research Scientist at Temasek Laboratories, National University of Singapore (NUS), Singapore. His research interest include Artificial Intelligence, Cyber physical systems, Text mining, Natural Language Processing, Machine learning, Deep learning, Intelligent Systems, Expert Systems and related areas. Valentina Emilia Balas is currently a Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, "Aurel Vlaicu? University of Arad, Romania. She holds a PhD cum Laude in Applied Electronics and Telecommunications from the Polytechnic University of Timisoara. Dr. Balas is the author of more than 350 research papers. She is the Editor-in-Chief of the 'International Journal of Advanced Intelligence Paradigms' and the 'International Journal of Computational Systems Engineering', an editorial board member for several other national and international publications, and an expert evaluator for national and international projects and PhD theses. Lakhmi C. Jain, BE(Hons), ME, PhD, Fellow (IE Australia) is with the Faculty of Education, Science, Technology & Mathematics at the University of Canberra, Australia and the University of Technology Sydney, Australia. He is a Fellow of the Institution of Engineers Australia.
Professor Jain founded the KES International for providing a professional community the opportunities for publications, knowledge exchange, cooperation and teaming. Involving around 5,000 researchers drawn from universities and companies world-wide, KES facilitates international cooperation and generate synergy in teaching and research. KES regularly provides networking opportunities for professional community through one of the largest conferences of its kind in the area of KES.
www.kesinternational.org
His interests focus on the artificial intelligence paradigms and their applications in complex systems, security, e-education, e-healthcare, unmanned air vehicles and intelligent agents. Working at Amity University, Jaipur, Rajasthan as Associate Professor in Amity Institute of Information Technology. Worked with Jaipur National University, Jaipur, Rajasthan as Assistant Professor in Department of Computer Science and Engineering. Worked with Stani Memorial College of Engineering and Technology, Phagi (Jaipur) as a Lecturer in the department of IT. Worked with Sri Balaji College of Engineering and Technology, Jaipur as a Lecturer in the department of IT. Worked with Mahrishi Computer & Management College, Sadulpur, Churu as a Lecturer in the Computer department. Currently teaching at CCT, University of Rajasthan. Worked as Associate Professor(Computer Science) in Department of Computer Science, Apaji Institute, Banasthali University. Worked as Sr. Assistant Professor and Assistant Professor(CS) in Department of Computer Science, Apaji Institute, Banasthali University. Worked as Programmer in Department of Computer Science, Apaji Institute, Banasthali University.
Professor Jain founded the KES International for providing a professional community the opportunities for publications, knowledge exchange, cooperation and teaming. Involving around 5,000 researchers drawn from universities and companies world-wide, KES facilitates international cooperation and generate synergy in teaching and research. KES regularly provides networking opportunities for professional community through one of the largest conferences of its kind in the area of KES.
www.kesinternational.org
His interests focus on the artificial intelligence paradigms and their applications in complex systems, security, e-education, e-healthcare, unmanned air vehicles and intelligent agents. Working at Amity University, Jaipur, Rajasthan as Associate Professor in Amity Institute of Information Technology. Worked with Jaipur National University, Jaipur, Rajasthan as Assistant Professor in Department of Computer Science and Engineering. Worked with Stani Memorial College of Engineering and Technology, Phagi (Jaipur) as a Lecturer in the department of IT. Worked with Sri Balaji College of Engineering and Technology, Jaipur as a Lecturer in the department of IT. Worked with Mahrishi Computer & Management College, Sadulpur, Churu as a Lecturer in the Computer department. Currently teaching at CCT, University of Rajasthan. Worked as Associate Professor(Computer Science) in Department of Computer Science, Apaji Institute, Banasthali University. Worked as Sr. Assistant Professor and Assistant Professor(CS) in Department of Computer Science, Apaji Institute, Banasthali University. Worked as Programmer in Department of Computer Science, Apaji Institute, Banasthali University.
Editor
Assistant Professor at the Indian Institute of Information Technology Kota (IIIT-Kota), India.
Full Professor, Department of Automatics and Applied Software, Faculty of Engineering, "Aurel Vlaicu" University of Arad, Arad, Romania
Faculty of Education, Science, Technology and Mathematics at the University of Canberra, Australia and the University of Technology Sydney, Australia
Associate Professor, Amity Institute of Information Technology, Amity University, Jaipur, Rajasthan, India
CCT, University of Rajasthan
Content
Part I: Deep Learning for Biomedical Engineering and Health Informatics
1. Introduction to Deep Learning and Health Informatics
2. A survey on deep learning algorithms for biomedical engineering
3. Machine learning and deep learning for Biomedical and Health Informatics
4. Deep learning for bioinformatics and drug discovery
5. Deep learning for Clinical Decision Support Systems
6. Deep learning for efficient Patients disease diagnosis and monitoring systems
7. Deep learning based methods for the Prediction of disease
8. Deep learning / Convolutional Neural Networks for Lung Pattern Analysis
9. Recommender systems for Biomedical and Health informatics
Part II: Deep Learning and Electronics Health Records
10. Deep Learning with Electronic Health Records (EHR)
11. Health Data Structures and Management
12. Deep Patient Similarity Learning with EHR
13. Natural Language Processing, Electronic Health Records, and Clinical Research
14. Healthcare Informatics to analyze patient health records to enable better clinical decision making and improved healthcare outcomes
Part III: Deep Learning for Medical Image Processing
15. Machine Learning in Bio-medical Signal and Medical image processing
16. Deep Learning for Medical Image Recognition
17. Unsupervised Deep Feature Representations Learning for Bio-medical Image analysis
18. Deep learning for optimizing medical big data
19. Deep learning for Brain Image Analysis
20. Deep Learning for Automated Brain Tumor Segmentation in MRI Images
21. Deep Learning and the Future of Biomedical Image Analysis
1. Introduction to Deep Learning and Health Informatics
2. A survey on deep learning algorithms for biomedical engineering
3. Machine learning and deep learning for Biomedical and Health Informatics
4. Deep learning for bioinformatics and drug discovery
5. Deep learning for Clinical Decision Support Systems
6. Deep learning for efficient Patients disease diagnosis and monitoring systems
7. Deep learning based methods for the Prediction of disease
8. Deep learning / Convolutional Neural Networks for Lung Pattern Analysis
9. Recommender systems for Biomedical and Health informatics
Part II: Deep Learning and Electronics Health Records
10. Deep Learning with Electronic Health Records (EHR)
11. Health Data Structures and Management
12. Deep Patient Similarity Learning with EHR
13. Natural Language Processing, Electronic Health Records, and Clinical Research
14. Healthcare Informatics to analyze patient health records to enable better clinical decision making and improved healthcare outcomes
Part III: Deep Learning for Medical Image Processing
15. Machine Learning in Bio-medical Signal and Medical image processing
16. Deep Learning for Medical Image Recognition
17. Unsupervised Deep Feature Representations Learning for Bio-medical Image analysis
18. Deep learning for optimizing medical big data
19. Deep learning for Brain Image Analysis
20. Deep Learning for Automated Brain Tumor Segmentation in MRI Images
21. Deep Learning and the Future of Biomedical Image Analysis