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Artificial intelligence and machine ...

Chandra, Shivani.

Artificial intelligence and machine learning in healthcare

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence and machine learning in healthcareedited by Ankur Saxena, Shivani Chandra.
other author:	Saxena, Ankur.
Published:	Singapore :Springer Singapore :2021.
Description:	xix, 228 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligenceMedical applications.
Online resource:	https://doi.org/10.1007/978-981-16-0811-7
ISBN:	9789811608117$q(electronic bk.)

Artificial intelligence and machine learning in healthcare
Artificial intelligence and machine learning in healthcare[electronic resource] /edited by Ankur Saxena, Shivani Chandra. - Singapore :Springer Singapore :2021. - xix, 228 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789811608117$q(electronic bk.)

Standard No.: 10.1007/978-981-16-0811-7doiSubjects--Topical Terms:

237917
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78

Dewey Class. No.: 610.28563

Artificial intelligence and machine learning in healthcare
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505 0 $a 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.
520 $a 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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