Machine learning and analytics in healthcare systems : Principles and applications / Edited by Himani Bansal1 et. al.

By:

Bansal, Himani

Contributor(s):

Material type: Text

TextLanguage: English Series: Green engineering and technology: concepts and applicationsPublication details: Boca Raton : CRC Press, 2021.Edition: 1st edDescription: xii, 261 p.: ill.; (chiefly color); 15.6 x 2.06 x 23.39 cmISBN:

9780367487935

Subject(s):

DDC classification:

23 006.31 BAN

Contents:

Chapter 1 Data Analytics in Healthcare Systems Principles, Challenges, and Applications Chapter 2 Systematic View and Impact of Machine Learning in Healthcare Systems Chapter 3 Foundation of Machine Learning-Based Data Classification Techniques for Health Care Chapter 4 Deep Learning for Computer-Aided Medical Diagnosis Chapter 5 Machine Learning Classifiers in Health Care Chapter 6 Machine Learning Approaches for Analysis in Healthcare Informatics Chapter 7 Prediction of Epidemic Disease Outbreaks, Using Machine Learning Chapter 8 Machine Learning-Based Case Studies for Healthcare Analytics: Electronic Health Records, Smart Health Monitoring, Disease Prediction, Precision Medicine, and Clinical Support Systems Chapter 9 Applications of Computational Methods and Modeling in Drug Delivery Chapter 10 Healthcare Data Analytics Using Business Intelligence ToolChapter 11 Machine Learning-Based Data Classification Techniques in Healthcare Using Massive Online Analysis FrameworkChapter 12 Prediction of Coronavirus (COVID-19) Disease Health Monitoring with Clinical Support System

Summary: This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine. It will combine the design and problem-solving skills of engineering with health sciences, in order to advance healthcare treatment. The book will include areas such as diagnosis, monitoring, and therapy. The book will provide real-world case studies, gives a detailed exploration of applications in healthcare systems, offers multiple perspectives on a variety of disciplines, while also letting the reader know how to avoid some of the consequences of old methods with data sharing. The book can be used as a reference for practitioners, researchers and for students at basic and intermediary levels in Computer Science, Electronics and Communications

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Holdings
Item type	Current library	Collection	Call number	Status	Barcode
General Books	CUTN Central Library Philosophy & psychology	Non-fiction	006.31 BAN (Browse shelf(Opens below))	Available	51258

Chapter 1 Data Analytics in Healthcare Systems
Principles, Challenges, and Applications
Chapter 2 Systematic View and Impact of Machine Learning in Healthcare Systems
Chapter 3 Foundation of Machine Learning-Based Data Classification Techniques for Health Care
Chapter 4 Deep Learning for Computer-Aided Medical Diagnosis
Chapter 5 Machine Learning Classifiers in Health Care
Chapter 6 Machine Learning Approaches for Analysis in Healthcare Informatics
Chapter 7 Prediction of Epidemic Disease Outbreaks, Using Machine Learning
Chapter 8 Machine Learning-Based Case Studies for Healthcare Analytics: Electronic Health Records, Smart Health Monitoring, Disease Prediction, Precision Medicine, and Clinical Support Systems
Chapter 9 Applications of Computational Methods and Modeling in Drug Delivery
Chapter 10 Healthcare Data Analytics Using Business Intelligence ToolChapter 11 Machine Learning-Based Data Classification Techniques in Healthcare Using Massive Online Analysis FrameworkChapter 12 Prediction of Coronavirus (COVID-19) Disease Health Monitoring with Clinical Support System

This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine. It will combine the design and problem-solving skills of engineering with health sciences, in order to advance healthcare treatment. The book will include areas such as diagnosis, monitoring, and therapy. The book will provide real-world case studies, gives a detailed exploration of applications in healthcare systems, offers multiple perspectives on a variety of disciplines, while also letting the reader know how to avoid some of the consequences of old methods with data sharing. The book can be used as a reference for practitioners, researchers and for students at basic and intermediary levels in Computer Science, Electronics and Communications

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