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…mehr
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.
Dr Gayathri Delanerolle, Senior Clinical Researcher, Southern Health NHS Foundation Trust, UK. Dr Yassine Bouchareb is a Registered Clinical Scientist, Health Care Professions Council, UK. As well as an Assistant Professor of Medical Physics, College of Medicine and Health Sciences, Sultan Qaboos University, Oman. Ashish Laxminarayana Shetty, Visiting Professor of Anaesthesiology & Pain Medicine, SSAHE, India. Shetty is also an Honorary Associate Professor, UCL, London and Chief Medical Officer (Pain and Neuromodulation) at NuroKor. Konstantinos V. Katsikopoulos, Professor of Behavioural Sciences and Founding Co-Director at the Center for Behavioral Experimental Action and Research, University of Southampton, UK. Peter Phiri, Director of Research & Innovation (Interim), Southern Health NHS Foundation Trust, UK.
Inhaltsangabe
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
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
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