Download or read book Data Analytics in Biomedical Engineering and Healthcare written by Kun Chang Lee and published by Academic Press. This book was released on 2020-10-18 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. - Examines the development and application of data analytics applications in biomedical data - Presents innovative classification and regression models for predicting various diseases - Discusses genome structure prediction using predictive modeling - Shows readers how to develop clinical decision support systems - Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks

Download or read book Handbook of Data Science Approaches for Biomedical Engineering written by Valentina Emilia Balas and published by Academic Press. This book was released on 2019-11-13 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress and the ways they are aligning with the latest technologies in IoT and big data. In addition, the book includes various real-time/offline medical applications that directly or indirectly rely on medical and information technology. Case studies in the field of medical science, i.e., biomedical engineering, computer science, information security, and interdisciplinary tools, along with modern tools and the technologies used are also included to enhance understanding. Today, the role of Big Data and IoT proves that ninety percent of data currently available has been generated in the last couple of years, with rapid increases happening every day. The reason for this growth is increasing in communication through electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, IoT, etc. - Provides in-depth information about Biomedical Engineering with Big Data and Internet of Things - Includes technical approaches for solving real-time healthcare problems and practical solutions through case studies in Big Data and Internet of Things - Discusses big data applications for healthcare management, such as predictive analytics and forecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, and more

Download or read book Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics written by Sunil Kumar Dhal and published by John Wiley & Sons. This book was released on 2022-05-20 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. Audience Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.

Download or read book Big Data Analytics in Bioinformatics and Healthcare written by Wang, Baoying and published by IGI Global. This book was released on 2014-10-31 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: As technology evolves and electronic data becomes more complex, digital medical record management and analysis becomes a challenge. In order to discover patterns and make relevant predictions based on large data sets, researchers and medical professionals must find new methods to analyze and extract relevant health information. Big Data Analytics in Bioinformatics and Healthcare merges the fields of biology, technology, and medicine in order to present a comprehensive study on the emerging information processing applications necessary in the field of electronic medical record management. Complete with interdisciplinary research resources, this publication is an essential reference source for researchers, practitioners, and students interested in the fields of biological computation, database management, and health information technology, with a special focus on the methodologies and tools to manage massive and complex electronic information.

Download or read book Leveraging Biomedical and Healthcare Data written by Firas Kobeissy and published by Academic Press. This book was released on 2018-11-23 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leveraging Biomedical and Healthcare Data: Semantics, Analytics and Knowledge provides an overview of the approaches used in semantic systems biology, introduces novel areas of its application, and describes step-wise protocols for transforming heterogeneous data into useful knowledge that can influence healthcare and biomedical research. Given the astronomical increase in the number of published reports, papers, and datasets over the last few decades, the ability to curate this data has become a new field of biomedical and healthcare research. This book discusses big data text-based mining to better understand the molecular architecture of diseases and to guide health care decision. It will be a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in understanding more about how to process and apply great amounts of data to improve their research. Includes at each section resource pages containing a list of available curated raw and processed data that can be used by researchers in the field Provides demonstrative and relevant examples that serve as a general tutorial Presents a list of algorithm names and computational tools available for basic and clinical researchers

Download or read book Data Analytics in Medicine Concepts Methodologies Tools and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2019-12-06 with total page 2071 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advancements in data science have created opportunities to sort, manage, and analyze large amounts of data more effectively and efficiently. Applying these new technologies to the healthcare industry, which has vast quantities of patient and medical data and is increasingly becoming more data-reliant, is crucial for refining medical practices and patient care. Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications is a vital reference source that examines practical applications of healthcare analytics for improved patient care, resource allocation, and medical performance, as well as for diagnosing, predicting, and identifying at-risk populations. Highlighting a range of topics such as data security and privacy, health informatics, and predictive analytics, this multi-volume book is ideally designed for doctors, hospital administrators, nurses, medical professionals, IT specialists, computer engineers, information technologists, biomedical engineers, data-processing specialists, healthcare practitioners, academicians, and researchers interested in current research on the connections between data analytics in the field of medicine.

Download or read book Healthcare Data Analytics written by Chandan K. Reddy and published by CRC Press. This book was released on 2015-06-23 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available

Download or read book Internet of Things in Biomedical Engineering written by Valentina Emilia Balas and published by Academic Press. This book was released on 2019-06-14 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Internet of Things in Biomedical Engineering presents the most current research in Internet of Things (IoT) applications for clinical patient monitoring and treatment. The book takes a systems-level approach for both human-factors and the technical aspects of networking, databases and privacy. Sections delve into the latest advances and cutting-edge technologies, starting with an overview of the Internet of Things and biomedical engineering, as well as a focus on 'daily life.' Contributors from various experts then discuss 'computer assisted anthropology,' CLOUDFALL, and image guided surgery, as well as bio-informatics and data mining. This comprehensive coverage of the industry and technology is a perfect resource for students and researchers interested in the topic. - Presents recent advances in IoT for biomedical engineering, covering biometrics, bioinformatics, artificial intelligence, computer vision and various network applications - Discusses big data and data mining in healthcare and other IoT based biomedical data analysis - Includes discussions on a variety of IoT applications and medical information systems - Includes case studies and applications, as well as examples on how to automate data analysis with Perl R in IoT

Download or read book Computational Learning Approaches to Data Analytics in Biomedical Applications written by Khalid Al-Jabery and published by Academic Press. This book was released on 2019-11-20 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained. - Includes an overview of data analytics in biomedical applications and current challenges - Updates on the latest research in supervised learning algorithms and applications, clustering algorithms and cluster validation indices - Provides complete coverage of computational and statistical analysis tools for biomedical data analysis - Presents hands-on training on the use of Python libraries, MATLAB® tools, WEKA, SAP-HANA and R/Bioconductor

Download or read book Healthcare Analytics written by Hui Yang and published by John Wiley & Sons. This book was released on 2016-10-10 with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt: Features of statistical and operational research methods and tools being used to improve the healthcare industry With a focus on cutting-edge approaches to the quickly growing field of healthcare, Healthcare Analytics: From Data to Knowledge to Healthcare Improvement provides an integrated and comprehensive treatment on recent research advancements in data-driven healthcare analytics in an effort to provide more personalized and smarter healthcare services. Emphasizing data and healthcare analytics from an operational management and statistical perspective, the book details how analytical methods and tools can be utilized to enhance healthcare quality and operational efficiency. Organized into two main sections, Part I features biomedical and health informatics and specifically addresses the analytics of genomic and proteomic data; physiological signals from patient-monitoring systems; data uncertainty in clinical laboratory tests; predictive modeling; disease modeling for sepsis; and the design of cyber infrastructures for early prediction of epidemic events. Part II focuses on healthcare delivery systems, including system advances for transforming clinic workflow and patient care; macro analysis of patient flow distribution; intensive care units; primary care; demand and resource allocation; mathematical models for predicting patient readmission and postoperative outcome; physician–patient interactions; insurance claims; and the role of social media in healthcare. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement also features: • Contributions from well-known international experts who shed light on new approaches in this growing area • Discussions on contemporary methods and techniques to address the handling of rich and large-scale healthcare data as well as the overall optimization of healthcare system operations • Numerous real-world examples and case studies that emphasize the vast potential of statistical and operational research tools and techniques to address the big data environment within the healthcare industry • Plentiful applications that showcase analytical methods and tools tailored for successful healthcare systems modeling and improvement The book is an ideal reference for academics and practitioners in operations research, management science, applied mathematics, statistics, business, industrial and systems engineering, healthcare systems, and economics. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement is also appropriate for graduate-level courses typically offered within operations research, industrial engineering, business, and public health departments.

Download or read book Medical Data Sharing Harmonization and Analytics written by Vasileios Pezoulas and published by Academic Press. This book was released on 2020-01-05 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical Data Sharing, Harmonization and Analytics serves as the basis for understanding the rapidly evolving field of medical data harmonization combined with the latest cloud infrastructures for storing the harmonized (shared) data. Chapters cover the latest research and applications on data sharing and protection in the medical domain, cohort integration through the recent advancements in data harmonization, cloud computing for storing and securing the patient data, and data analytics for effectively processing the harmonized data. Examines the unmet needs in chronic diseases as a part of medical data sharing Discusses ethical, legal and privacy issues as part of data protection Combines data harmonization and big data analytics strategies in shared medical data, along with relevant case studies in chronic diseases

Download or read book Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications written by Om Prakash Jena and published by CRC Press. This book was released on 2022-02-25 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features: Covers the fundamentals of ML and DL in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in ML/DL models Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios. Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India. Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.

Download or read book Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics written by Pradeep N and published by Academic Press. This book was released on 2021-06-10 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians. - Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies - Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics - Unique case study approach provides readers with insights for practical clinical implementation

Download or read book Health Informatics Data Analysis written by Dong Xu and published by Springer. This book was released on 2017-09-08 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of different biomedical data types, including both clinical and genomic data. Thorough explanations enable readers to explore key topics ranging from electrocardiograms to Big Data health mining and EEG analysis techniques. Each chapter offers a summary of the field and a sample analysis. Also covered are telehealth infrastructure, healthcare information association rules, methods for mass spectrometry imaging, environmental biodiversity, and the global nonlinear fitness function for protein structures. Diseases are addressed in chapters on functional annotation of lncRNAs in human disease, metabolomics characterization of human diseases, disease risk factors using SNP data and Bayesian methods, and imaging informatics for diagnostic imaging marker selection. With the exploding accumulation of Electronic Health Records (EHRs), there is an urgent need for computer-aided analysis of heterogeneous biomedical datasets. Biomedical data is notorious for its diversified scales, dimensions, and volumes, and requires interdisciplinary technologies for visual illustration and digital characterization. Various computer programs and servers have been developed for these purposes by both theoreticians and engineers. This book is an essential reference for investigating the tools available for analyzing heterogeneous biomedical data. It is designed for professionals, researchers, and practitioners in biomedical engineering, diagnostics, medical electronics, and related industries.

Download or read book Big Data Analytics for Healthcare written by Pantea Keikhosrokiani and published by Academic Press. This book was released on 2022-05-19 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics and Medical Information Systems presents the valuable use of artificial intelligence and big data analytics in healthcare and medical sciences. It focuses on theories, methods and approaches in which data analytic techniques can be used to examine medical data to provide a meaningful pattern for classification, diagnosis, treatment, and prediction of diseases. The book discusses topics such as theories and concepts of the field, and how big medical data mining techniques and applications can be applied to classification, diagnosis, treatment, and prediction of diseases. In addition, it covers social, behavioral, and medical fake news analytics to prevent medical misinformation and myths. It is a valuable resource for graduate students, researchers and members of biomedical field who are interested in learning more about analytic tools to support their work. - Presents theories, methods and approaches in which data analytic techniques are used for medical data - Brings practical information on how to use big data for classification, diagnosis, treatment, and prediction of diseases - Discusses social, behavioral, and medical fake news analytics for medical information systems

Download or read book Big Data Big Challenges A Healthcare Perspective written by Mowafa Househ and published by Springer. This book was released on 2019-02-26 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book to offer a comprehensive yet concise overview of the challenges and opportunities presented by the use of big data in healthcare. The respective chapters address a range of aspects: from health management to patient safety; from the human factor perspective to ethical and economic considerations, and many more. By providing a historical background on the use of big data, and critically analyzing current approaches together with issues and challenges related to their applications, the book not only sheds light on the problems entailed by big data, but also paves the way for possible solutions and future research directions. Accordingly, it offers an insightful reference guide for health information technology professionals, healthcare managers, healthcare practitioners, and patients alike, aiding them in their decision-making processes; and for students and researchers whose work involves data science-related research issues in healthcare.

Download or read book Biomedical Engineering written by Sang C. Suh and published by Springer Science & Business Media. This book was released on 2011-08-23 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biomedical Engineering: Health Care Systems, Technology and Techniques is an edited volume with contributions from world experts. It provides readers with unique contributions related to current research and future healthcare systems. Practitioners and researchers focused on computer science, bioinformatics, engineering and medicine will find this book a valuable reference.