Machine Learning and Analytics in Healthcare Systems: Principles and Applications

This book combines the design and problem-solving skills of engineering with health sciences to advance healthcare treatment. It will include areas such as diagnosis, monitoring, and therapy and will feature real-world case studies, detailed exploration of applications, and multiple perspectives on disciplines. It can be used as a reference for practitioners, researchers, and students.

Format: Hardback
Length: 262 pages
Publication date: 01 July 2021
Publisher: Taylor & Francis Ltd

This comprehensive book delves into the realm of machine learning, exploring its applications in healthcare systems with the aim of bridging the gap between engineering and medicine. By seamlessly integrating the design and problem-solving skills of engineering with the knowledge of health sciences, the book strives to advance healthcare treatment to new heights. The book will cover a wide range of areas, including diagnosis, monitoring, and therapy, providing valuable insights into the field.

In order to provide a practical and comprehensive understanding of machine learning in healthcare, the book will feature real-world case studies that showcase its practical applications. These case studies will not only illustrate the potential benefits of machine learning but also highlight the challenges and limitations associated with its implementation. Moreover, the book will offer a detailed exploration of applications in healthcare systems, drawing from a diverse range of disciplines. This multidisciplinary approach ensures that readers gain a comprehensive understanding of the field, enabling them to apply machine learning techniques to address complex healthcare problems.

Furthermore, the book recognizes the importance of data sharing in the field of machine learning. It encourages readers to adopt ethical practices and guidelines when dealing with sensitive healthcare data, ensuring that privacy and confidentiality are maintained at all times. By promoting data sharing, the book aims to facilitate the development of more accurate and effective machine learning models, ultimately benefiting patients and healthcare professionals alike.

Designed with practitioners, researchers, and students in Computer Science, Electronics, and Communications in mind, this book serves as a valuable reference for individuals seeking to expand their knowledge in machine learning and its applications in healthcare. It provides a solid foundation in machine learning principles, algorithms, and techniques, enabling readers to apply these knowledge to real-world scenarios. Whether you are a healthcare professional looking to enhance your understanding of machine learning or a student seeking to explore the latest advancements in this field, this book is an essential resource for you.

Weight: 550g
Dimension: 160 x 243 x 22 (mm)
ISBN-13: 9780367487935