Data Analytics in Biomedical Engineering and Healthcare

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  • Author : Kun Chang Lee
  • Publisher : Academic Press
  • Pages : 292 pages
  • ISBN : 0128193158
  • Rating : /5 from reviews
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Download or Read online Data Analytics in Biomedical Engineering and Healthcare full in PDF, ePub and kindle. this book written by Kun Chang Lee and published by Academic Press which was released on 23 October 2020 with total page 292 pages. We cannot guarantee that Data Analytics in Biomedical Engineering and Healthcare book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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

Data Analytics in Biomedical Engineering and Healthcare

Data Analytics in Biomedical Engineering and Healthcare
  • Author : Kun Chang Lee,Sanjiban Sekhar Roy,Pijush Samui,Vijay Kumar
  • Publisher : Academic Press
  • Release : 23 October 2020
GET THIS BOOK Data Analytics in Biomedical Engineering and Healthcare

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

Big Data Analytics in Bioinformatics and Healthcare

Big Data Analytics in Bioinformatics and Healthcare
  • Author : Wang, Baoying
  • Publisher : IGI Global
  • Release : 31 October 2014
GET THIS BOOK Big Data Analytics in Bioinformatics and Healthcare

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

Deep Learning for Data Analytics

Deep Learning for Data Analytics
  • Author : Himansu Das,Chittaranjan Pradhan,Nilanjan Dey
  • Publisher : Academic Press
  • Release : 29 May 2020
GET THIS BOOK Deep Learning for Data Analytics

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear

Handbook of Data Science Approaches for Biomedical Engineering

Handbook of Data Science Approaches for Biomedical Engineering
  • Author : Valentina E. Balas,Vijender Kumar Solanki,Raghvendra Kumar,Manju Khari
  • Publisher : Academic Press
  • Release : 13 November 2019
GET THIS BOOK Handbook of Data Science Approaches for Biomedical Engineering

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

Data Analytics in Medicine

Data Analytics in Medicine
  • Author : Information Resources Management Association
  • Publisher : Medical Information Science Reference
  • Release : 18 November 2019
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""This book 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"--

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare
  • Author : Janmenjoy Nayak,Bighnaraj Naik,Danilo Pelusi,Asit Kumar Das
  • Publisher : Academic Press
  • Release : 08 April 2021
GET THIS BOOK Handbook of Computational Intelligence in Biomedical Engineering and Healthcare

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surrounding oncology, genomics and genetic data, ontologies construction, bio-memetic systems, biomedical electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced computational intelligence, spanning deep learning, fuzzy logic, connectionist systems, evolutionary computation, cellular automata, self-organizing systems, soft computing, and hybrid intelligent systems in biomedical and healthcare applications. Sections focus on important

Biomedical Engineering

Biomedical Engineering
  • Author : Sang C. Suh,Varadraj Gurupur,Murat M. Tanik
  • Publisher : Springer Science & Business Media
  • Release : 23 August 2011
GET THIS BOOK Biomedical Engineering

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.

Healthcare Data Analytics and Management

Healthcare Data Analytics and Management
  • Author : Nilanjan Dey,Amira S. Ashour,Simon James Fong,Chintan Bhatt
  • Publisher : Academic Press
  • Release : 15 November 2018
GET THIS BOOK Healthcare Data Analytics and Management

Healthcare Data Analytics and Management help readers disseminate cutting-edge research that delivers insights into the analytic tools, opportunities, novel strategies, techniques and challenges for handling big data, data analytics and management in healthcare. As the rapidly expanding and heterogeneous nature of healthcare data poses challenges for big data analytics, this book targets researchers and bioengineers from areas of machine learning, data mining, data management, and healthcare providers, along with clinical researchers and physicians who are interested in the management and

Applications of Big Data in Healthcare

Applications of Big Data in Healthcare
  • Author : Ashish Khanna,Deepak Gupta,Nilanjan Dey
  • Publisher : Academic Press
  • Release : 10 March 2021
GET THIS BOOK Applications of Big Data in Healthcare

Applications of Big Data in Healthcare: Theory and Practice begins with the basics of Big Data analysis and introduces the tools, processes and procedures associated with Big Data analytics. The book unites healthcare with Big Data analysis and uses the advantages of the latter to solve the problems faced by the former. The authors present the challenges faced by the healthcare industry, including capturing, storing, searching, sharing and analyzing data. This book illustrates the challenges in the applications of Big

Internet of Things in Biomedical Engineering

Internet of Things in Biomedical Engineering
  • Author : Valentina E. Balas,Le Hoang Son,Sudan Jha,Manju Khari,Raghvendra Kumar
  • Publisher : Academic Press
  • Release : 14 June 2019
GET THIS BOOK Internet of Things in Biomedical Engineering

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,

IoT Based Data Analytics for the Healthcare Industry

IoT Based Data Analytics for the Healthcare Industry
  • Author : Sanjay Kumar Singh,Ravi Shankar Singh,Anil Kumar Pandey,Sandeep S Udmale,Ankit Chaudhary
  • Publisher : Academic Press
  • Release : 01 December 2020
GET THIS BOOK IoT Based Data Analytics for the Healthcare Industry

IoT Based Data Analytics for the Healthcare Industry: Techniques and Applications explores recent advances in the analysis of healthcare industry data through IoT data analytics. The book covers the analysis of ubiquitous data generated by the healthcare industry, from a wide range of sources, including patients, doctors, hospitals, and health insurance companies. The book provides AI solutions and support for healthcare industry end-users who need to analyze and manipulate this vast amount of data. These solutions feature deep learning and

Data Science and Predictive Analytics

Data Science and Predictive Analytics
  • Author : Ivo D. Dinov
  • Publisher : Springer
  • Release : 07 April 2018
GET THIS BOOK Data Science and Predictive Analytics

Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. There are three reasons for this shortfall. First, the volume of data is

Handbook of Deep Learning in Biomedical Engineering

Handbook of Deep Learning in Biomedical Engineering
  • Author : Valentina Emilia Balas,Brojo Kishore Mishra,Raghvendra Kumar
  • Publisher : Academic Press
  • Release : 23 November 2020
GET THIS BOOK Handbook of Deep Learning in Biomedical Engineering

Deep learning (DL) is a method of machine learning, running over artificial neural networks, that uses multiple layers to extract high-level features from large amounts of raw data. DL methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of DL and its applications in the field of biomedical engineering. DL has been rapidly developed in

Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics

Demystifying Big Data  Machine Learning  and Deep Learning for Healthcare Analytics
  • Author : Pradeep Nijalingappa,Sandeep Kautish,Sheng Lung Peng
  • Publisher : Academic Press
  • Release : 01 June 2021
GET THIS BOOK Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents how these emerging areas are changing the world of data utilization, especially in clinical healthcare. Various techniques, methodologies and algorithms are presented in a structured manner to assist physicians in the precision care of patients and help biomedical engineers and computers scientists understand the impact of these techniques on healthcare analytics. Sections cover Big Data aspects, i.e., healthcare Decision Support Systems and Analytics related topics, focus on

Predictive Intelligence in Biomedical and Health Informatics

Predictive Intelligence in Biomedical and Health Informatics
  • Author : Rajshree Srivastava,Nhu Gia Nguyen,Ashish Khanna,Siddhartha Bhattacharyya
  • Publisher : Unknown
  • Release : 12 October 2020
GET THIS BOOK Predictive Intelligence in Biomedical and Health Informatics

Predictive Intelligence in Biomedical and Health Informatics focuses on imaging, computer-aided diagnosis and therapy as well as intelligent biomedical image processing and analysis. It develops computational models, methods and tools for biomedical engineering related to computer-aided diagnostics (CAD), computer-aided surgery (CAS), computational anatomy and bioinformatics. Large volumes of complex data are often a key feature of biomedical and engineering problems and computational intelligence helps to address such problems. Practical and validated solutions to hard biomedical and engineering problems can be