Handbook on Intelligent Healthcare Analytics. Группа авторов

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      Preface

      The power of healthcare data analytics is being increasingly used in the industry. With this in mind, we wanted to write a book geared towards those who want to learn more about the techniques used in healthcare analytics for efficient analysis of data. Since data is generally generated in enormous amounts and pumped into data pools, analyzing data patterns can help to ensure a better quality of life for patients. As a result of small amounts of health data from patients suffering from various health issues being collectively pooled, researchers and doctors can find patterns in the statistics, helping them develop new ways of forecasting or diagnosing health issues, and identifying possible ways to improve quality clinical care. Big data analytics supports this research by applying various processes to examine large and varied healthcare data sets. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. This book covers both the theory and application of the tools, techniques and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare. In addition, this book also explores various sources of personalized healthcare data.

For those who are healthcare researchers, this book reveals the innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets. Since machine learning algorithms play a major role in analyzing the volume, veracity and velocity of big data, the scope of this book focuses on various kinds of machine learning algorithms existing in the areas such as supervised, unsupervised, semi-supervised, and reinforcement learning. It guides readers in implementing the Python environment for machine learning in various application domains. Furthermore, predictive analytics in healthcare is explored, which can help to detect early signs of patient deterioration from the ICU to a general ward, identify at-risk patients in their homes to prevent hospital readmissions, and prevent avoidable downtime of medical equipment.

      Also explored in the book are a wide variety of machine learning techniques that can be applied to infer intelligence from the data set and the capabilities of an application. The significance of data sets for various applications is also discussed along with sample case studies. Moreover, the challenges presented by the techniques and budding research avenues necessary to see their further advancement are highlighted.

      Patient’s healthcare data needs to be protected by organizations in order to prevent data loss through unauthorized access. This data needs to be protected from attacks that can encrypt or destroy data, such as ransomware, as well as those attacks that can modify or corrupt a patient’s data. Security is paramount since a lot of devices are connected through the internet of things and serve many healthcare applications, including supporting smart healthcare systems in the management of various diseases such as diabetes, monitoring heart functions, predicting heart failure, etc. Therefore, this book explores the various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., which create a new burden for providers to maintain compliance with healthcare data security.

      In addition to inferring knowledge fusion patterns in healthcare, the book also explores the commercial platforms for healthcare data analytics. The new benefits that healthcare data analytics brings to the table, run analytics and unearth information that could be used in the decision-making of practitioners by providing insights that can be used to make immediate decisions. Also investigated are the new trends and applications of big data analytics for medical science and healthcare. Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.

      Editors

       Dr. A. Jaya

       Dr. K. Kalaiselvi*

       Dr. Dinesh Goyal

       Prof. Dhiya AL-Jumeily

       *Corresponding Editor