This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare.

Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

Contents

• Introduction “Fundamentals of Clinical Data Science”
• I. Data Collection
  • 1. Data Sources
    Pieter Kubben.
    Published online: December 22, 2018.
    • 1.1. Data Sources
    • 1.2. GDPR
    • 1.3. Data Types
    • 1.4. Data Standards
    • 1.5. Conclusion
    • References
  • 2. Data at Scale
    Alberto Traverso, Frank J. W. M. Dankers, Leonard Wee, and Sander M. J. van Kuijk.
    Published online: December 22, 2018.
    • 2.1. Introduction
    • 2.2. ‘Big’ Clinical Data: The Four ‘Vs’
    • 2.3. Data Landscape
    • 2.4. Barriers to Big Data Exchange
    • 2.5. Conclusion
    • References
  • 3. Standards in Healthcare Data
    Stefan Schulz, Robert Stegwee, and Catherine Chronaki.
    Published online: December 22, 2018.
    • 3.1. Introduction
    • 3.2. Implementation of Standards
    • 3.3. Conclusion
    • References
  • 4. Research Data Stewardship for Healthcare Professionals
    Paula Jansen, Linda van den Berg, Petra van Overveld, and Jan-Willem Boiten.
    Published online: December 22, 2018.
    • 4.1. Data Stewardship: What, Why, How, and Who?
    • 4.2. Preparing a Study
    • 4.3. Privacy and Autonomy
    • 4.4. Collecting Data
    • 4.5. Analysing Data
    • 4.6. Archiving Data
    • 4.7. Sharing Data
    • 4.8. Conclusion
    • Acknowledgments
    • References
  • 5. The EU’s General Data Protection Regulation (GDPR) in a Research Context
    Christopher F. Mondschein and Cosimo Monda.
    Published online: December 22, 2018.
    • 5.1. Introduction
    • 5.2. Data Protection Law in the EU
    • 5.3. The GDPR
    • 5.4. Scope of Application of the GDPR
    • 5.5. Key Concepts of the GDPR
    • 5.6. The GDPR’s Research Exemption
    • 5.7. Contentious Issues for Research Under the GDPR
    • 5.8. Checklists
    • 5.9. Conclusion
    • References
• II. From Data to Model
  • 6. Preparing Data for Predictive Modelling
    Sander M. J. van Kuijk, Frank J. W. M. Dankers, Alberto Traverso, and Leonard Wee.
    Published online: December 22, 2018.
    • 6.1. Introduction
    • 6.2. Study Designs for Prediction Model Development
    • 6.3. Sample Size Considerations
    • 6.4. Pre-processing Your Data
    • 6.5. Missing Data
    • References
  • 7. Extracting Features from Time Series
    Christian Herff and Dean J. Krusienski.
    Published online: December 22, 2018.
    • 7.1. Time-Domain Processing
    • 7.2. Frequency-Domain Processing
    • 7.3. Time-Frequency Processing: Wavelets
    • 7.4. Conclusion
    • References
  • 8. Prediction Modeling Methodology
    Frank J. W. M. Dankers, Alberto Traverso, Leonard Wee, and Sander M. J. van Kuijk.
    Published online: December 22, 2018.
    • 8.1. Statistical Hypothesis Testing
    • 8.2. Creating a Prediction Model Using Regression Techniques
    • 8.3. Creating a Model That Performs Well Outside the Training Set
    • 8.4. Model Performance Metrics
    • 8.5. Validation of a Prediction Model
    • 8.6. Summary Remarks
    • References
  • 9. Diving Deeper into Models
    Alberto Traverso, Frank J. W. M. Dankers, Biche Osong, Leonard Wee, and Sander M. J. van Kuijk.
    Published online: December 22, 2018.
    • 9.1. Introduction
    • 9.2. What Is Machine Learning?
    • 9.3. How Do We Use Machine Learning in Clinical Prediction Modelling?
    • 9.4. Supervised Algorithms
    • 9.5. Unsupervised Algorithms
    • 9.6. Semi-supervised Algorithms
    • 9.7. Supervised Algorithms
    • 9.8. Unsupervised Algorithms
    • 9.9. Conclusion
    • References
  • 10. Reporting Standards and Critical Appraisal of Prediction Models
    Leonard Wee, Sander M. J. van Kuijk, Frank J. W. M. Dankers, Alberto Traverso, Mattea Welch, and Andre Dekker.
    Published online: December 22, 2018.
    • 10.1. Introduction
    • 10.2. Prediction Modelling Studies
    • 10.3. Reporting Your Own Work
    • 10.4. Critical Appraisal of Published Models
    • 10.5. Conclusion
    • References
• III. From Model to Application
  • 11. Clinical Decision Support Systems
    A. T. M. Wasylewicz and A. M. J. W. Scheepers-Hoeks.
    Published online: December 22, 2018.
    • 11.1. Introduction on CDSS
    • 11.2. Challenges for Implementing a CDSS
    • 11.3. Best Knowledge & Continuous Improvement of Knowledge and CDSS Methods
    • 11.4. Future Perspectives
    • References
  • 12. Mobile Apps
    Pieter Kubben.
    Published online: December 22, 2018.
    • 12.1. Operating Systems
    • 12.2. Collecting Health Data
    • 12.3. Mobile Clinical Decision Support Systems
    • 12.4. Software as a Medical Device
    • 12.5. Conclusion
    • References
  • 13. Optimizing Care Processes with Operational Excellence & Process Mining
    Henri J. Boersma, Tiffany I. Leung, Rob Vanwersch, Elske Heeren, and G. G. van Merode.
    Published online: December 22, 2018.
    • 13.1. Introduction
    • 13.2. Care Process
    • 13.3. Operational Excellence
    • 13.4. Process Mining
    • 13.5. Sociotechnical Systems & Leadership
    • 13.6. Conclusion
    • References
  • 14. Value-Based Health Care Supported by Data Science
    Tiffany I. Leung and G. G. van Merode.
    Published online: December 22, 2018.
    • 14.1. Introduction
    • 14.2. Measuring Outcomes
    • 14.3. Measuring Cost
    • 14.4. Creating Value Through Innovation
    • 14.5. Increasing Value in a Learning Health System
    • 14.6. Sociotechnical Considerations
    • 14.7. Further Considerations in Measuring Value
    • References