Artificial Intelligence and Machine Learning in Healthcare

1. Auflage 2021
ISBN: 978-981-16-0810-0
Verlag: Springer Nature Singapore

Buch, Englisch, 228 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 586 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Chandra, Shivani

Saxena, Ankur

Fachgebiete

• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Betriebliches Gesundheitsmanagement
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Public Health, Gesundheitsmanagement, Gesundheitsökonomie, Gesundheitspolitik

Inhaltsverzeichnis

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.

Über Autor(innen)

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.