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Full Description
This book brings together everything you need to know about data science within healthcare systems, with a primary focus on showing how to advance automated and non-automated analytical methods for extracting valuable insights from healthcare data.
It draws upon a range of interconnected disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web Technologies. The book emphasizes the practical application of these disciplines in the healthcare domain inclusive of quality assurance, governance and regulatory overview. It includes instructional chapters on data science in healthcare as a foundation, then progresses to showcase real world, successful examples of data science and AI applications in healthcare, highlighting their range of usefulness and potential.
Intended primarily for healthcare professionals, including clinical academics, academics and trainees working in the healthcare or medical sectors, this book offers crucial insights into cutting-edge data science technologies, essential for driving innovation in both healthcare businesses and patient care.
Contents
Part I: An Introduction to Data Science
1. Brief History
2. Data Science in Medicine
3. Data Science for Clinical Practice
4. Data Science and Application Use
5. Human Factors and Data Science
6. Case Study
Part II: Data Science and Artificial Intelligence
7. Introduction to AI
8. Machine Learning and Model Development
9. Deep Learning and Model Development
10. Algorithm Development as Clinical Decision Making Tools
11. Evidence-Based Medicine Methods to Model Data Science
12. Clinical Trials for AI Tools
13. Developing AI as Effectiveness Tools
14. The Use of Big Data and Data Platforms
15. Case Study
Part III: Ethical Implications and Social Policy
16. Introduction to Data Science and Ethics
17. Ethical Issues and Legislation Development
18. Patient-Public Involvement and Engagement
19. Data Science and Social Policy
20. Case Study
Part IV: Medical Statistics
21. Introduction to Medical Statistics
22. Epidemiology Model Development
23. Epidemiology Model Validation and their Constraints in Medicine
24. Epidemiology Models for Data Augmentation
25. Synthetic Data Development and Modelling
26. Introduction to Clinical Trial Statistics
27. Gaussian Methodology and Application in Clinical Epidemiology
28. Bayesian Methodology and Application in Clinical Epidemiology
29. Case Study
Part V: Application Development Using Data Science
30. Digital Medicine Tool Development
31. Mobile Applications as Clinician Decision Aids
32. Real-World Data Tool for Real-Time Data Gathering
33. Precision Medicine Tool for Predicting Outcomes
34. Software Development Using Data Science Principles
35. Simulation Tools for Medical Education
36. Robotic Surgery Using Data Applications
37. Cognitive Performance Applications
38. Case Study
Part VI: Governance and Regulatory Approvals
39. Quality Assurance, Quality Control, and Quality Management
40. Quality Indicators and Continuous Improvement
41. Research Governance
42. Data Codes of Practice and Frameworks
43. Developing Data Governance and Regulatory Frameworks
44. Audits and Regulatory Inspection Preparation
45. Case Study
