Artificial Intelligence and Machine Learning in Healthcare
1st Edition
Published on 6. May 2021
XV, 228 pages
978-981-16-0811-7 (ISBN)
Description
This book reviews the application of artificial intelligence and machine learning in healthcare. It discusses integrating the principles of computer science, life science, and statistics incorporated into statistical models using existing data, discovering patterns in data to extract the information, and predicting the changes and diseases based on this data and models. The initial chapters of the book cover the practical applications of artificial intelligence for disease prognosis & management. Further, the role of artificial intelligence and machine learning is discussed with reference to specific diseases like diabetes mellitus, cancer, mycobacterium tuberculosis, and Covid-19. The chapters provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and artificial intelligence. The book also touches upon precision medicine, personalized medicine, and transfer learning, with the real examples. Further, it also discusses the use of machine learning and artificial intelligence for visualization, prediction, detection, and diagnosis of Covid -19. This book is a valuable source of information for programmers, healthcare professionals, and researchers interested in understanding the applications of artificial intelligence and machine learning in healthcare.
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Ankur Saxena | Shivani Chandra
Artificial Intelligence and Machine Learning in Healthcare
Book
05/2021
Springer
€160.49
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Persons
Dr. Ankur Saxena is currently working as an Assistant Professor at Amity University, Noida, Uttar Pradesh. He has been teaching graduate and post-graduate students for more than 15 years and 3 years of industrial experience in software development. He has published 5 books and more than 40 research articles in reputed journals and is an editorial board member and reviewer for several journals. His research interests include cloud computing, big data, machine learning, evolutionary algorithms, software frameworks, design & analysis of algorithms, and biometric identification.
Dr. Shivani Chandra is an Assistant Professor at Amity Institute of Biotechnology, Amity University, Uttar Pradesh, Noida. She has more than 20 years of experience in biotechnology and molecular biology. Her research interests includes genomics analysis, computational biology, and bioinformatics data analysis. She has submitted more than 4000 clones to the NCBI GenBank and was one of the key players in the Rice Genome Sequencing Project. She has published several research articles in genome sequencing, comparative genomics, and genome analysis in reputed journals. She has more than 15 years of teaching experience in computational biology, molecular biology, genetics, recombinant DNA Technology, and bioinformatics.
Dr. Shivani Chandra is an Assistant Professor at Amity Institute of Biotechnology, Amity University, Uttar Pradesh, Noida. She has more than 20 years of experience in biotechnology and molecular biology. Her research interests includes genomics analysis, computational biology, and bioinformatics data analysis. She has submitted more than 4000 clones to the NCBI GenBank and was one of the key players in the Rice Genome Sequencing Project. She has published several research articles in genome sequencing, comparative genomics, and genome analysis in reputed journals. She has more than 15 years of teaching experience in computational biology, molecular biology, genetics, recombinant DNA Technology, and bioinformatics.
Content
Chapter 1_Big Data Analytics and AI for Healthcare.- Chapter 2_Genetics with Big Data and AI.- Chapter 3_AI and Big Data for next-generation sequencing.- Chapter 4_Artificial Intelligence for Computational Biology.- Chapter 5_Artificial intelligence and machine learning in clinical development.- Chapter 6_Big data analytics for personalized medicine.- Chapter 7_Generating and Managing Healthcare data with AI.- Chapter 8_Big Data and Artificial Intelligence for diseases.- Chapter 9_Artificial Intelligence and Big Data for Public Health.- Chapter 10_Biasness in Healthcare Big Data and Computational Algorithms.- Chapter 11_AI and ML in Healthcare: An Ethical perspective.
