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Data Analytics in Biomedical Engineering and Healthcare

  • ID: 5018804
  • Book
  • October 2020
  • Region: Global
  • 220 Pages
  • Elsevier Science and Technology
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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
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1. Data analytics applications in biomedical data 2. Predictive Health Analysis 3. Exploration of EHR (Electronic Health Records) using data science 4. Machine Learning and Deep Learning application on medical image analysis 5. Developing Clinical Decision Support System 6. Innovative Classification, Regression Model for predicting various diseases 7. Computational Drug Discovery using State of the Art Unsupervised learning 8. Genome Structure prediction using Predictive modelling 9. Hybrid learning for better medical diagnosis 10. Big data application in healthcare under MapReduce and Hadoop frameworks

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Lee, Kun Chang
1995-To date Full Professor SKK Business School Sungkyunkwan University

Responsible for teaching Business Datamining, MIS (Management Information Systems), and Internet Business Models in undergraduate and graduate. I conduct several director positions for executive programs with Samsung Group. I am the quadruple winner of "The Sungkyunkwan University Outstanding Research Award”. In 2006 I received the university's highest research honor, "The Sungkyunkwan University Fellow Award" in recognition of extraordinary accomplishment in research and scholarship. Accordingly, I was honorably included in the Hall of Fame of the SKK Business School in 2007.
Roy, Sanjiban Sekhar
Sanjiban Sekhar Roy is a Senior Associate Professor at the School of Computer Science and Engineering at VIT University. His primary courses focus on algorithm design and analysis, computer programming and problem solving, knowledge based systems, and agent based intelligence. He has published numerous journal articles on neural computing, neural networks, support vector machines, image processing and pattern recognition. He serves as a reviewer for the International Journal of Advanced Intelligence Paradigms and the International Journal of Artificial Intelligence and Soft Computing. He has received three publication awards for his research from VIT University.
Samui, Pijush
Dr Pijush Samui is Associate Professor, Department of Civil Engineering, NIT Patna, Bihar, India
Kumar, Vijay
Dr. Vijay Kumar, Senior Member IEEE MTT APS and GARS HEAD DST-SERB Microwave and Radar Imaging Laboratory and Associate Professor, Microwave and Photonics Group School of Electronics and Communication Engineering Vellore Institute of Technology, Vellore, TN, India.
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