Machine Learning in Healthcare
Fundamentals and Recent Applications
1st Edition
Published on 8. October 2024
Book
Paperback/Softback
226 pages
978-0-367-56443-8 (ISBN)
Description
Artificial intelligence (AI) and machine learning (ML) techniques play an important role in our daily lives by enhancing predictions and decision-making for the public in several fields such as financial services, real estate business, consumer goods, social media, etc. Despite several studies that have proved the efficacy of AI/ML tools in providing improved healthcare solutions, it has not gained the trust of health-care practitioners and medical scientists. This is due to poor reporting of the technology, variability in medical data, small datasets, and lack of standard guidelines for application of AI. Therefore, the development of new AI/ML tools for various domains of medicine is an ongoing field of research.
Machine Learning in Healthcare: Fundamentals and Recent Applications discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis, early detection and diagnosis of disease, providing objective-based evidence to reduce human errors, curtailing inter- and intra-observer errors, risk identification and interventions for healthcare management, real-time health monitoring, assisting clinicians and patients for selecting appropriate medications, and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided, along with solved examples and exercises.
This text is ideal for readers interested in machine learning without any background knowledge and looking to implement machine-learning models for healthcare systems.
Machine Learning in Healthcare: Fundamentals and Recent Applications discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis, early detection and diagnosis of disease, providing objective-based evidence to reduce human errors, curtailing inter- and intra-observer errors, risk identification and interventions for healthcare management, real-time health monitoring, assisting clinicians and patients for selecting appropriate medications, and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided, along with solved examples and exercises.
This text is ideal for readers interested in machine learning without any background knowledge and looking to implement machine-learning models for healthcare systems.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Academic
Illustrations
69 s/w Zeichnungen, 13 s/w Tabellen, 24 s/w Photographien bzw. Rasterbilder, 93 s/w Abbildungen
13 Tables, black and white; 69 Line drawings, black and white; 24 Halftones, black and white; 93 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 14 mm
Weight
392 gr
ISBN-13
978-0-367-56443-8 (9780367564438)
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
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Book
02/2022
1st Edition
CRC Press
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02/2022
1st Edition
CRC Press
€63.49
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E-Book
02/2022
1st Edition
CRC Press
€63.49
Available for download
Persons
Dr Bikesh Kumar Singh is Assistant Professor in the Department of Biomedical Engineering at the National Institute of Technology Raipur, India, where he also received his Ph.D. in Biomedical Engineering. He has twelve years of teaching experience, and for five years he served as the Head of the Department of Biomedical Engineering. He has published more than seventy research papers in various international journals and conferences. He is the recipient of the Chhattisgarh Young Scientist Award, IETE Gowri Memorial Award, IEI Young Engineer Award.
Dr G.R. Sinha is Adjunct Professor at the International Institute of Information Technology Bangalore (IIITB) and deputed as Professor at Myanmar Institute of Information Technology (MIIT) Mandalay Myanmar. He has been Visiting Professor (Honorary) in Sri Lanka Technological Campus Colombo during 2019-2020. He has been Visiting Professor for teaching Short Graduate Course on Cognitive Science and Brain Computing Research at University of Sannio Italy during September 2020-March 2021.
He has published 275 research papers, book chapters and books at International level that includes Biometrics published by Wiley India, a subsidiary of John Wiley; Medical Image Processing published by Prentice Hall of India and 13 Edited books. He is Associate Editor of five SCI/Scopus indexed journals. He has teaching and research experience of 22 years. He has been Dean of Faculty and Executive Council Member of CSVTU and currently a member of Senate of MIIT. Dr Sinha has been delivering ACM lectures as ACM Distinguished Speaker in the field of DSP since 2017 across the world. His few more important assignments include Expert Member for Vocational Training Program by Tata Institute of Social Sciences (TISS) for Two Years (2017-2019); Chhattisgarh Representative of IEEE MP Sub-Section Executive Council (2014-2017); Distinguished Speaker in the field of Digital Image Processing by Computer Society of India (2015). He served as Distinguished IEEE Lecturer in IEEE India council for Bombay section.
He is recipient of more than 12 awards and recognitions at National and International levels. He has delivered more than 50 Keynote/Invited Talks and Chaired many Technical Sessions in International Conferences across the world such as Singapore, Myanmar, Sri Lanka, Irvine, Italy and India. He is Consultant of various Skill Development initiatives of NSDC, Govt. of India. He is regular Referee of Project Grants under DST-EMR scheme and several other schemes of Govt. of India.
Dr G.R. Sinha is Adjunct Professor at the International Institute of Information Technology Bangalore (IIITB) and deputed as Professor at Myanmar Institute of Information Technology (MIIT) Mandalay Myanmar. He has been Visiting Professor (Honorary) in Sri Lanka Technological Campus Colombo during 2019-2020. He has been Visiting Professor for teaching Short Graduate Course on Cognitive Science and Brain Computing Research at University of Sannio Italy during September 2020-March 2021.
He has published 275 research papers, book chapters and books at International level that includes Biometrics published by Wiley India, a subsidiary of John Wiley; Medical Image Processing published by Prentice Hall of India and 13 Edited books. He is Associate Editor of five SCI/Scopus indexed journals. He has teaching and research experience of 22 years. He has been Dean of Faculty and Executive Council Member of CSVTU and currently a member of Senate of MIIT. Dr Sinha has been delivering ACM lectures as ACM Distinguished Speaker in the field of DSP since 2017 across the world. His few more important assignments include Expert Member for Vocational Training Program by Tata Institute of Social Sciences (TISS) for Two Years (2017-2019); Chhattisgarh Representative of IEEE MP Sub-Section Executive Council (2014-2017); Distinguished Speaker in the field of Digital Image Processing by Computer Society of India (2015). He served as Distinguished IEEE Lecturer in IEEE India council for Bombay section.
He is recipient of more than 12 awards and recognitions at National and International levels. He has delivered more than 50 Keynote/Invited Talks and Chaired many Technical Sessions in International Conferences across the world such as Singapore, Myanmar, Sri Lanka, Irvine, Italy and India. He is Consultant of various Skill Development initiatives of NSDC, Govt. of India. He is regular Referee of Project Grants under DST-EMR scheme and several other schemes of Govt. of India.
Content
1. Biostatistics. 2. Probability Theory. 3. Medical Data Acquisition and Pre-processing. 4. Medical Image Processing. 5. Bio-signals. 6. Feature Extraction. 7. Introduction to Machine Learning. 8. Cancer detection: Breast Cancer Detection Using Mammography, Ultrasound and Magnetic Resonance Imaging (MRI). 9. Sickle Cell Disease Management: A Machine Learning Approach. 10. Detection of Pulmonary Diseases. 11. Mental Illness and Neurodevelopmental Disorders. 12. Applications and Challenges.