Biocomputation and biomedical informatics / U.S. Raghavender.

Cover; Half Title Page; Title Page; Copyright Page; Dedication; About the Author; Table of Contents; Preface; Chapter 1 Biomedical Informatics; 1.1. What is Informatics?; 1.2. A Brief History of Informatics; 1.3. Key Areas of Informatics; 1.4. Informatics Career Options; 1.5. Biomedical Informatics Sub-Fields; 1.6. Working in The Field; 1.7. Management in Health Industry; 1.8. IT Project Managers; 1.9. Career Outlook for Healthcare IT Project Managers; 1.10. Healthcare IT Project Manager Education; 1.11. Public Health Informatics; 1.12. Public Health Management

1.13. Understanding Population Health Management1.14. Where Big Data Comes Into Play; 1.15. What are EHR's?; 1.16. Electronic Health Records: The Past, Present, and Future; 1.17. Where we are Now with Electronic Health Records?; 1.18. The Future of Electronic Health Records; 1.19 What are Electronic Medical Records?; 1.20. What are Electronic Health Records?; 1.21. How Do Electronic Medical Records Differ From Electronic Health Records?; 1.22. 3D Imaging in Healthcare Delivery; 1.23. Medical Informatics; 1.24. Types of Work in Medical Informatics

1.25. Specialties and Careers in the Field of Medical Informatics1.26. What is Bioinformatics?; 1.27. Working in Bioinformatics; 1.28. Conclusions and Trends in 2018; Chapter 2 Python: An Introduction to Scientific Computing; 2.1. The Role of Computing in Science and Informatics; 2.2. Requirements on Scientific Computing; 2.3. Python; 2.4. Python for Scientific Computing; 2.5. Regular Expressions; 2.6. High Performance Computing; 2.7. Conclusions; Chapter 3 Sequence Alignments in Biology; 3.1. Local Alignment; 3.2. BLAST; 3.3. Working of BLAST Algorithm; 3.4. Accessing Databases

3.5. Running BLAST Over The Internet3.6. Saving BLAST Output; 3.7. Graphics Including Genomediagram; 3.8. Chromosomes; 3.9. Conclusions; Chapter 4 Natural Language Processing (NLP); 4.1. What is NLP?; 4.2. Simple Clinical Natural Language Processing with Pycontextnlp; 4.3. Specifying Targets, Modifiers, and Rules; 4.4. Processing Multisentence Documents; 4.5. Classifying Documents; 4.6. Document Classification; 4.7. Annotation Coloring Scheme; 4.8. Define Our Domain Terms; 4.9. Document Markup and Classification; 4.10. Radiology NLP; 4.11. Conclusions; Chapter 5 Deep Learning

5.1. What is Deep Learning?5.2. Inside Deep Learning; 5.3. Core Concepts; 5.4. Convolutional Deep Learning; 5.5. An Example -- Multilayer Perceptrons; 5.6. A First Model with Logistic Regression; 5.7. Implementing Basics in Deep Learning; Chapter 6 Patient Care: A Practical Computational Approach; 6.1. Overview of MIMIC; 6.2. MIMIC Database; 6.3. Visualizations; 6.4. Feature Selection and Dimensionality Reduction; 6.5. Principal Component Analysis; 6.6. t- Distributed Stochastic Neighbor Embedding (t-SNE); 6.7. Visualization; 6.8. Preprocessing; 6.9. Results; 6.10. Improved Model with Temporal Features.

Includes bibliographical references and index.

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