Data Science in Healthcare

A Complete Guide

AvGayathri Delanerolle,Yassine Bouchareb

Häftad, Engelska, 2026

Del i serien Chapman & Hall/CRC Data Science Series

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Beskrivning

Data science is transforming every aspect of modern life, from healthcare and urban planning to finance and governance. This comprehensive book provides an accessible yet rigorous exploration of the principles, methodologies, and applications of data science. It introduces readers to the evolution of data science, the role of artificial intelligence, and the philosophical foundations underpinning data‑driven discovery. By bridging theory and practice, this book provides a unique perspective on how data can be harnessed to solve complex, real‑world problems.Unlike other texts, this book integrates technical foundations with ethical, regulatory, and societal considerations. It covers the entire data science lifecycle, from acquisition and processing to advanced analytics, application development, and decision support. Real‑world case studies—including a detailed example of using electronic health records in Rwanda—illustrate how data science can drive impact in low‑ and high‑resource settings alike.Key Features:Comprehensive coverage of data science fundamentals, machine learning, and advanced analyticsIn‑depth exploration of ethical, governance, and regulatory challengesPractical insights into developing applications and decision support systemsGlobal case studies demonstrating applied data science in healthcare and public policyClear explanations suitable for multidisciplinary audiencesThis book is ideal for students, researchers, and professionals in data science, healthcare, public policy, and technology development. It equips readers with both the technical and ethical tools needed to navigate today’s data‑rich world and to leverage data for innovation, problem‑solving, and societal good.

Produktinformation

Utgivningsdatum:2026-07-27
Mått:156 x 234 x undefined mm
Vikt:453 g
Format:Häftad
Språk:Engelska
Serie:Chapman & Hall/CRC Data Science Series
Antal sidor:250
Förlag:Taylor & Francis Ltd
ISBN:9781032786063

Utforska kategorier

Hälso- och sjukvård inom Medicin
Databaser inom Data och IT
Artificiell intelligens inom Data och IT

Mer om författaren

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.

Innehållsförteckning

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 StudyPart 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 StudyPart 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 StudyPart 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