Artificial Intelligence and Data Mining in Healthcare - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Artificial Intelligence and Data Mining in Healthcare write by Malek Masmoudi. This book was released on 2021-01-25. Artificial Intelligence and Data Mining in Healthcare available in PDF, EPUB and Kindle. This book presents recent work on healthcare management and engineering using artificial intelligence and data mining techniques. Specific topics covered in the contributed chapters include predictive mining, decision support, capacity management, patient flow optimization, image compression, data clustering, and feature selection. The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.
Artificial Intelligence in Healthcare
Artificial Intelligence in Healthcare - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Artificial Intelligence in Healthcare write by Adam Bohr. This book was released on 2020-06-21. Artificial Intelligence in Healthcare available in PDF, EPUB and Kindle. Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data
Machine Learning and AI for Healthcare
Machine Learning and AI for Healthcare - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning and AI for Healthcare write by Arjun Panesar. This book was released on 2019-02-04. Machine Learning and AI for Healthcare available in PDF, EPUB and Kindle. Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.
Biomedical Data Mining for Information Retrieval
Biomedical Data Mining for Information Retrieval - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Biomedical Data Mining for Information Retrieval write by Sujata Dash. This book was released on 2021-08-06. Biomedical Data Mining for Information Retrieval available in PDF, EPUB and Kindle. BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.
Demystifying Big Data and Machine Learning for Healthcare
Demystifying Big Data and Machine Learning for Healthcare - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Demystifying Big Data and Machine Learning for Healthcare write by Prashant Natarajan. This book was released on 2017-02-15. Demystifying Big Data and Machine Learning for Healthcare available in PDF, EPUB and Kindle. Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.
