Data Analytics in Biomedical Engineering and Healthcare
- 292 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Data Analytics in Biomedical Engineering and Healthcare
About this book
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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Information
Table of contents
- 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