Machine Learning in Biomedical Science and Healthcare Informatics – serie

“Emerging Technologies for Healthcare” begins with an IoT-based solution for the automated healthcare sector which is enhanced to provide solutions with advanced deep learning techniques.The book provides feasible solutions through various machine learning approaches and applies them to disease analysis and prediction. An example of this is employing a three-dimensional matrix approach for treating chronic kidney disease, the diagnosis and prognostication of acquired demyelinating syndrome (ADS) and autism spectrum disorder, and the detection of pneumonia. In addition, it provides healthcare solutions for post COVID-19 outbreaks through various suitable approaches, Moreover, a detailed detection mechanism is discussed which is used to devise solutions for predicting personality through handwriting recognition; and novel approaches for sentiment analysis are also discussed with sufficient data and its dimensions.This book not only covers theoretical approaches and algorithms, but also contains the sequence of steps used to analyze problems with data, processes, reports, and optimization techniques. It will serve as a single source for solving various problems via machine learning algorithms.

HANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners. The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. A Handbook on Intelligent Healthcare Analytics 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, the reader will find in this Handbook: Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning;An exploration of predictive analytics in healthcare;The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics.AudienceHealthcare 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.

COMPUTATIONAL INTELLIGENCE and HEALTHCARE INFORMATICS The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis. Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments. This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis. Audience The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.

This book is an essential resource on the impact of AI in medical systems, helping readers stay ahead in the modern era with cutting-edge solutions, knowledge, and real-world case studies. Wellness Management Powered by AI Technologies explores the intricate ways machine learning and the Internet of Things (IoT) have been woven into the fabric of healthcare solutions. From smart wearable devices tracking vital signs in real time to ML-driven diagnostic tools providing accurate predictions, readers will gain insights into how these technologies continually reshape healthcare. The book begins by examining the fundamental principles of machine learning and IoT, providing readers with a solid understanding of the underlying concepts. Through clear and concise explanations, readers will grasp the complexities of the algorithms that power predictive analytics, disease detection, and personalized treatment recommendations. In parallel, they will uncover the role of IoT devices in collecting data that fuels these intelligent systems, bridging the gap between patients and practitioners. In the following chapters, readers will delve into real-world case studies and success stories that illustrate the tangible benefits of this dynamic duo. This book is not merely a technical exposition; it serves as a roadmap for healthcare professionals and anyone invested in the future of healthcare. Readers will find the book: Explores how AI is transforming diagnostics, treatments, and healthcare delivery, offering cutting-edge solutions for modern healthcare challenges;Provides practical knowledge on implementing AI in healthcare settings, enhancing efficiency and patient outcomes;Offers authoritative insights into current AI trends and future developments in healthcare;Features real-world case studies and examples showcasing successful AI integrations in various medical fields.AudienceThis book is a valuable resource for researchers, industry professionals, and engineers from diverse fields such as computer science, artificial intelligence, electronics and electrical engineering, healthcare management, and policymakers.

The book is essential for anyone exploring the forefront of healthcare innovation, as it offers a thorough exploration of transformative data-driven methodologies that can significantly enhance patient outcomes and clinical efficiency in today’s evolving medical landscape. In today’s rapidly advancing healthcare landscape, the integration of medical analytics has become essential for improving patient outcomes, clinical efficiency, and decision-making. Medical Analytics for Clinical and Healthcare Applications provides a comprehensive examination of how data-driven methodologies are revolutionizing the medical field. This book offers a deep dive into innovative techniques, real-world applications, and emerging trends in medical analytics, showcasing how these advancements are transforming disease detection, diagnosis, treatment planning, and healthcare management. Spanning sixteen chapters across five subsections, this edited volume covers a wide array of topics—from foundational principles of medical data analysis to cutting-edge applications in predictive healthcare and medical data security. Readers will encounter state-of-the-art methodologies, including machine learning models, predictive analytics, and deep learning techniques applied to various healthcare challenges such as mental health disorders, cancer detection, and hospital mortality predictions. Medical Analytics for Clinical and Healthcare Applications equips readers with the knowledge to harness the power of medical analytics and its potential to shape the future of healthcare. Through its interdisciplinary approach and expert insights, this volume is poised to serve as a valuable resource for advancing healthcare technologies and improving the overall quality of care. Readers will find the volume: Explores the latest medical analytics techniques applied across clinical settings, from diagnosis to treatment optimization;Features real-world case studies and tools for implementing data-driven solutions in healthcare;Bridges the gap between healthcare professionals, data scientists, and engineers for collaborative innovation in medical technologies;Provides foresight into emerging trends and technologies shaping the future of healthcare analytics.Audience Healthcare professionals, clinical researchers, medical data scientists, biomedical engineers, IT professionals, academics, and policymakers focused on the intersection of medicine and data analytics.