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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Yes, you can access Data Analytics in Biomedical Engineering and Healthcare by Kun Chang Lee,Sanjiban Sekhar Roy,Pijush Samui,Vijay Kumar in PDF and/or ePUB format, as well as other popular books in Sciences biologiques & Biotechnologie. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Academic Press

Year

2020

ISBN

9780128193150

Topic

Sciences biologiques

Subtopic

Biotechnologie

Cover image
Title page
Table of Contents
Copyright
Dedication
Contributors
Preface
1: Estimate sequential poses for wireless endoscopic capsule based on encoder-decoder convolutional neural network toward autonomous navigation
2: Interoperability issues in EHR systems: Research directions
3: Difficulty with language comprehension and arithmetic word problems due to hearing impairment: Analysis and a possible remedy through a new Android-based assistive technology
4: Machine learning in healthcare toward early risk prediction: A case study of liver transplantation
5: Utilizing BERT for biomedical and clinical text mining
6: Classifying CT scan images based on contrast material and age of a person: ConvNets approach
7: Data analytics in IOT-based health care
8: Application of PCA based unsupervised FE to neurodegenerative diseases
9: Disease diagnosis using machine learning: A comparative study
10: Driver drowsiness detection using heart rate and behavior methods: A study
11: Innovative classification, regression model for predicting various diseases
12: Clavicle bone segmentation from CT images using U-Net-based deep learning algorithm
13: Accurate classification of heart sounds for disease diagnosis by using spectral analysis and deep learning methods
14: Complex neutrosophic δ-equal concepts and their applications in water quality
Index