Full Description
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 are applied to Biomedical Engineering, Electronic Health Records, and medical image processing.
Contents
Part I: Deep Learning for Biomedical Engineering and Health Informatics
1. Introduction to Deep Learning and Health Informatics
2. A survey on deep learning algorithms for biomedical engineering
3. Machine learning and deep learning for Biomedical and Health Informatics
4. Deep learning for bioinformatics and drug discovery
5. Deep learning for Clinical Decision Support Systems
6. Deep learning for efficient Patients disease diagnosis and monitoring systems
7. Deep learning based methods for the Prediction of disease
8. Deep learning / Convolutional Neural Networks for Lung Pattern Analysis
9. Recommender systems for Biomedical and Health informatics
Part II: Deep Learning and Electronics Health Records
10. Deep Learning with Electronic Health Records (EHR)
11. Health Data Structures and Management
12. Deep Patient Similarity Learning with EHR
13. Natural Language Processing, Electronic Health Records, and Clinical Research
14. Healthcare Informatics to analyze patient health records to enable better clinical decision making and improved healthcare outcomes
Part III: Deep Learning for Medical Image Processing
15. Machine Learning in Bio-medical Signal and Medical image processing
16. Deep Learning for Medical Image Recognition
17. Unsupervised Deep Feature Representations Learning for Bio-medical Image analysis
18. Deep learning for optimizing medical big data
19. Deep learning for Brain Image Analysis
20. Deep Learning for Automated Brain Tumor Segmentation in MRI Images
21. Deep Learning and the Future of Biomedical Image Analysis
