Description
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.
Table of 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 Tool
Chapter 11 Machine Learning-Based Data Classification Techniques in Healthcare Using Massive Online Analysis Framework
Chapter 12 Prediction of Coronavirus (COVID-19) Disease Health Monitoring with Clinical Support System and its Objectives
Index
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- 洋書電子書籍
- My Antonia
