A Handbook of Intelligent Healthcare Analytics - Knowledge Engineering with Big Data

The Handbook of Intelligent Healthcare Analytics provides an in-depth exploration of the latest tools and techniques used for deriving knowledge from healthcare data analytics,including hybrid machine learning and deep learning techniques,predictive analytics,and challenges for smart healthcare. It is designed for healthcare professionals, researchers, and practitioners who want to understand the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare.

Format: Hardback
Length: 448 pages
Publication date: 24 May 2022
Publisher: John Wiley & Sons Inc

The book delves into the diverse array of recent tools and techniques employed for extracting valuable insights from healthcare data analytics, catering to both researchers and practitioners in the field. The utilization of healthcare data analytics is gaining significant traction within the industry. Advanced analytics methodologies are employed to analyze vast datasets, uncovering concealed patterns, untapped correlations, market trends, customer preferences, and other valuable information.

A Handbook on Intelligent Healthcare Analytics encompasses both the theoretical underpinnings and practical applications of the tools, techniques, and algorithms utilized in the realm of big data in healthcare and clinical research. It serves as a comprehensive resource, presenting the most recent research findings to derive knowledge through the utilization of big data analytics. This analysis empowers healthcare professionals to effectively analyze substantial amounts of real-time healthcare data, offering valuable insights that can contribute to improvements in various aspects of healthcare, including procedural, technical, medical, and others.

Furthermore, this Handbook offers a comprehensive exploration of innovative hybrid machine learning and deep learning techniques applied across diverse healthcare data sets. It also provides an in-depth examination of various machine learning algorithms, including supervised, unsupervised, semi-supervised, reinforcement learning, and guides on how readers can set up a Python environment for machine learning.

Additionally, the book delves into the realm of predictive analytics in healthcare, shedding light on the use of data-driven models to anticipate and analyze healthcare outcomes. It addresses the various challenges posed by smart healthcare, such as privacy, confidentiality, authenticity, information loss, and attacks, which create new obligations for providers to uphold compliance with healthcare data security regulations. Furthermore, this Handbook explores diverse sources of personalized healthcare data and the commercial platforms available for healthcare data analytics.

The intended audience for this Handbook encompasses healthcare professionals, researchers, and students interested in leveraging the power of healthcare data analytics to drive innovation and improve patient outcomes.

Weight: 754g
Dimension: 231 x 161 x 29 (mm)
ISBN-13: 9781119791799