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 18 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 : 18 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

Handbook of Data Science Approaches for Biomedical Engineering

Handbook of Data Science Approaches for Biomedical Engineering
  • Author : Valentina Emilia 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

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

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications
  • Author : Om Prakash Jena,Bharat Bhushan,Utku Kose
  • Publisher : CRC Press
  • Release : 25 February 2022
GET THIS BOOK Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications

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

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

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
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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,

Semantic Web for Effective Healthcare Systems

Semantic Web for Effective Healthcare Systems
  • Author : Vishal Jain,Jyotir Moy Chatterjee,Ankita Bansal,Abha Jain
  • Publisher : John Wiley & Sons
  • Release : 12 November 2021
GET THIS BOOK Semantic Web for Effective Healthcare Systems

Recently, the Semantic Web has gained huge popularity to address these challenges. Semantic web technologies have the opportunity to transform the way healthcare providers utilize technology to gain insights and knowledge from their data and make decisions. Both big data and semantic web technologies can complement each other to address the challenges and add intelligence to healthcare management systems. The aim of this book is to analyze the current status on how Semantic Web is used to solve the health

Green Computing and Predictive Analytics for Healthcare

Green Computing and Predictive Analytics for Healthcare
  • Author : Sourav Banerjee,Chinmay Chakraborty,Kousik Dasgupta
  • Publisher : CRC Press
  • Release : 10 December 2020
GET THIS BOOK Green Computing and Predictive Analytics for Healthcare

Green Computing and Predictive Analytics for Healthcare excavates the rudimentary concepts of Green Computing, Big Data and the Internet of Things along with the latest research development in the domain of healthcare. It also covers various applications and case studies in the field of computer science with state-of-the-art tools and technologies. The rapid growth of the population is a challenging issue in maintaining and monitoring various experiences of quality of service in healthcare. The coherent usage of these limited resources

Smart Computational Intelligence in Biomedical and Health Informatics

Smart Computational Intelligence in Biomedical and Health Informatics
  • Author : Amit Kumar Manocha,Mandeep Singh,Shruti Jain,Vishal Jain
  • Publisher : CRC Press
  • Release : 27 September 2021
GET THIS BOOK Smart Computational Intelligence in Biomedical and Health Informatics

Smart Computational Intelligence in Biomedical and Health Informatics presents state-of-the-art innovations; research, design, and implementation of methodological and algorithmic solutions to data processing problems, including analysis of evolving trends in health informatics and computer-aided diagnosis. This book describes practical, applications-led research regarding the use of methods and devices in clinical diagnosis, disease prevention, and patient monitoring and management. It also covers simulation and modeling, measurement and control, analysis, information extraction and monitoring of physiological data in clinical medicine and the

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.

Intelligent Data Analysis for Biomedical Applications

Intelligent Data Analysis for Biomedical Applications
  • Author : Hemanth D. Jude,Deepak Gupta,Valentina Emilia Balas
  • Publisher : Academic Press
  • Release : 15 March 2019
GET THIS BOOK Intelligent Data Analysis for Biomedical Applications

Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions presents specialized statistical, pattern recognition, machine learning, data abstraction and visualization tools for the analysis of data and discovery of mechanisms that create data. It provides computational methods and tools for intelligent data analysis, with an emphasis on problem-solving relating to automated data collection, such as computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and more. This book provides useful references for educational institutions, industry professionals, researchers,

Demystifying Big Data Machine Learning and Deep Learning for Healthcare Analytics

Demystifying Big Data  Machine Learning  and Deep Learning for Healthcare Analytics
  • Author : Pradeep N,Sandeep Kautish,Sheng-Lung Peng
  • Publisher : Academic Press
  • Release : 25 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 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

Health Informatics Data Analysis

Health Informatics Data Analysis
  • Author : Dong Xu,May D. Wang,Fengfeng Zhou,Yunpeng Cai
  • Publisher : Springer
  • Release : 08 September 2017
GET THIS BOOK Health Informatics Data Analysis

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

Data Science for Healthcare

Data Science for Healthcare
  • Author : Sergio Consoli,Diego Reforgiato Recupero,Milan Petković
  • Publisher : Springer
  • Release : 22 March 2019
GET THIS BOOK Data Science for Healthcare

This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight