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 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

Produk Detail:

  • Author : Kun Chang Lee
  • Publisher : Academic Press
  • Pages : 292 pages
  • ISBN : 0128193158
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKData Analytics in Biomedical Engineering and Healthcare

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 BOOKData 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

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 BOOKHandbook 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 BOOKDeep 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

Deep Learning Techniques for Biomedical and Health Informatics

Deep Learning Techniques for Biomedical and Health Informatics
  • Author : Dr. Basant Agarwal,Valentina E. Balas,Lakhmi C. Jain,Ramesh Chandra Poonia,Manisha Sharma
  • Publisher : Academic Press
  • Release : 14 January 2020
GET THIS BOOKDeep Learning Techniques for Biomedical and Health Informatics

Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they

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 BOOKInternet 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,

Data Science and Predictive Analytics

Data Science and Predictive Analytics
  • Author : Ivo D. Dinov
  • Publisher : Springer
  • Release : 07 April 2018
GET THIS BOOKData 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 Artificial Intelligence in Biomedical Engineering

Handbook of Artificial Intelligence in Biomedical Engineering
  • Author : Saravanan Krishnan,Ramesh Kesavan,B. Surendiran,G.S. Mahalakshmi
  • Publisher : CRC Press
  • Release : 30 March 2021
GET THIS BOOKHandbook of Artificial Intelligence in Biomedical Engineering

Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications. This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert’s knowledge in order to predict and monitor

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 BOOKBig 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

Computational Topology for Biomedical Image and Data Analysis

Computational Topology for Biomedical Image and Data Analysis
  • Author : Rodrigo Rojas Moraleda,Nektarios A. Valous,Wei Xiong,Niels Halama
  • Publisher : CRC Press
  • Release : 12 July 2019
GET THIS BOOKComputational Topology for Biomedical Image and Data Analysis

This book provides an accessible yet rigorous introduction to topology and homology focused on the simplicial space. It presents a compact pipeline from the foundations of topology to biomedical applications. It will be of interest to medical physicists, computer scientists, and engineers, as well as undergraduate and graduate students interested in this topic. Features: Presents a practical guide to algebraic topology as well as persistence homology Contains application examples in the field of biomedicine, including the analysis of histological images

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 BOOKIntelligent 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,

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 BOOKHealthcare 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

Sentiment Analysis in the Bio-Medical Domain

Sentiment Analysis in the Bio-Medical Domain
  • Author : Ranjan Satapathy,Erik Cambria,Amir Hussain
  • Publisher : Springer
  • Release : 23 January 2018
GET THIS BOOKSentiment Analysis in the Bio-Medical Domain

The abundance of text available in social media and health-related forums and blogs have recently attracted the interest of the public health community to use these sources for opinion mining. This book presents a lexicon-based approach to sentiment analysis in the bio-medical domain, i.e., WordNet for Medical Events (WME). This book gives an insight in handling unstructured textual data and converting it to structured machine-processable data in the bio-medical domain. The readers will discover the following key novelties: 1) development

Medical Data Sharing, Harmonization and Analytics

Medical Data Sharing, Harmonization and Analytics
  • Author : Vasileios Pezoulas,Themis Exarchos,Dimitrios I Fotiadis
  • Publisher : Academic Press
  • Release : 05 January 2020
GET THIS BOOKMedical Data Sharing, Harmonization and Analytics

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

Terahertz Biomedical and Healthcare Technologies

Terahertz Biomedical and Healthcare Technologies
  • Author : Amit Banerjee,Basabi Chakraborty,Hiroshi Inokawa,Jitendra Nath Roy
  • Publisher : Elsevier
  • Release : 11 August 2020
GET THIS BOOKTerahertz Biomedical and Healthcare Technologies

Terahertz Biomedical and Healthcare Technologies: Materials to Devices reviews emerging advances in terahertz biomedical and healthcare technologies, including advances in fundamental materials science research, device design and fabrication, applications, and challenges and opportunities for improved performance. In addition, the improvement of materials, optical elements, and measuring techniques are also explored. Other sections cover the design and development of wide bandgap semiconductors for terahertz device applications, including their physics, device modeling, characterization and fabrication concepts. Finally, the book touches on potential

Healthcare Data Analytics

Healthcare Data Analytics
  • Author : Chandan K. Reddy,Charu C. Aggarwal
  • Publisher : CRC Press
  • Release : 23 June 2015
GET THIS BOOKHealthcare Data Analytics

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 to solve healthcare problems. The book details novel techniques for acquiring, handling, retrieving, and making best use of healthcare data. It analyzes recent developments in healthcare computing and discusses emerging technologies that can help improve