{"title":"Machine Learning In Biomedical Science And Healthcare Informatics","description":"\u003cp\u003eExplore the cutting edge of healthcare with this collection on machine learning. Discover how AI is transforming biomedical science and informatics, offering new solutions and insights for the future of medicine.\u003c\/p\u003e","products":[{"product_id":"handbook-on-intelligent-healthcare-analytics-book-a-jaya-9781119791799","title":"Handbook on Intelligent Healthcare Analytics","description":"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.   Audience  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.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ GARDNERS","offer_id":49739733303569,"sku":"NGR9781119791799","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/1119791790.jpg?v=1751301946"},{"product_id":"internet-of-healthcare-things-book-kavita-sharma-9781119791768","title":"Internet of Healthcare Things","description":"INTERNET OF HEALTHCARE THINGS The book addresses privacy and security issues providing solutions through authentication and authorization mechanisms, blockchain, fog computing, machine learning algorithms, so that machine learning-enabled IoT devices can deliver information concealed in data for fast, computerized responses and enhanced decision-making.   The main objective of this book is to motivate healthcare providers to use telemedicine facilities for monitoring patients in urban and rural areas and gather clinical data for further research. To this end, it provides an overview of the Internet of Healthcare Things (IoHT) and discusses one of the major threats posed by it, which is the data security and data privacy of health records. Another major threat is the combination of numerous devices and protocols, precision time, data overloading, etc. In the IoHT, multiple devices are connected and communicate through certain protocols. Therefore, the application of emerging technologies to mitigate these threats and provide secure data communication over the network is discussed. This book also discusses the integration of machine learning with the IoHT for analyzing huge amounts of data for predicting diseases more accurately. Case studies are also given to verify the concepts presented in the book.     Audience   Researchers and industry engineers in computer science, artificial intelligence, healthcare sector, IT professionals, network administrators, cybersecurity experts.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ GARDNERS","offer_id":50463913148689,"sku":"NGR9781119791768","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/1119791766.jpg?v=1750951888"},{"product_id":"medical-analytics-for-clinical-and-healthcare-applications-book-xiaozhi-gao-9781394301454","title":"Medical Analytics for Clinical and Healthcare Applications","description":"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.","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51604467351825,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ GARDNERS","offer_id":51604467613969,"sku":"NGR9781394301454","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/1394301456.jpg?v=1763634290"}],"url":"https:\/\/www.worldofbooks.com\/collections\/machine-learning-in-biomedical-science-and-healthcare-informatics-book-series.oembed","provider":"World of Books ","version":"1.0","type":"link"}