UID:
almahu_9949602261002882
Format:
1 online resource (218 pages)
Edition:
1st ed.
ISBN:
9783319997131
Note:
Intro -- Introduction "Fundamentals of Clinical Data Science" -- Contents -- Part I: Data Collection -- Chapter 1: Data Sources -- 1.1 Data Sources -- 1.1.1 Electronic Medical Records -- 1.1.2 Other Medical Information Systems -- 1.1.3 Mobile Apps -- 1.1.4 Internet of Things and Big Data -- 1.1.5 Social Media -- 1.2 GDPR -- 1.3 Data Types -- 1.3.1 Tabular Data -- 1.3.2 Time Series -- 1.3.3 Natural Language -- 1.3.4 Images and Videos -- 1.4 Data Standards -- 1.5 Conclusion -- References -- Chapter 2: Data at Scale -- 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 -- Chapter 3: Standards in Healthcare Data -- 3.1 Introduction -- 3.1.1 Data and Reality -- 3.1.2 Desiderata for Clinical Data Standards -- 3.1.3 Aspects of Terminology, Syntax, Semantics and Pragmatics -- 3.1.4 Representational Artefacts for Standardising Clinical Data -- 3.1.5 Quality and Usability of Standards -- 3.2 Implementation of Standards -- 3.2.1 Tools and Standards for Standards -- 3.2.2 The eHealth Standards Roadmap -- 3.2.2.1 Trust and Flow: The Basis of Well-Functioning Health Systems -- 3.2.2.2 eStandards Compass to Respect Different Perspectives of Stakeholders -- 3.2.2.3 eStandards Roadmap Components: Reusing eHealth Artefacts -- 3.2.2.4 CGA Model: Co-creation, Governance and Alignment -- 3.2.3 The eStandards Roadmap Methodology at Work -- 3.3 Conclusion -- References -- (Web publications last accessed on June, 19th, 2018) -- Chapter 4: Research Data Stewardship for Healthcare Professionals -- 4.1 Data Stewardship: What, Why, How, and Who? -- 4.1.1 Definitions -- 4.1.2 Why? -- 4.1.3 FAIR Principles -- 4.1.4 Responsibilities -- 4.2 Preparing a Study -- 4.2.1 Study Design and Registration -- 4.2.2 Re-using Existing Data -- 4.2.3 Collaborating with Patients.
,
4.2.4 Data Management Plan and Statistical Analysis Plan -- 4.2.5 Describing the Operational Workflow -- 4.2.6 Choosing File Formats -- 4.2.7 Intellectual Property Rights -- 4.2.8 Data Access -- 4.3 Privacy and Autonomy -- 4.3.1 Informed Consent -- 4.3.2 Care and Research Environment -- 4.3.3 Preparing Sensitive Data for Use -- 4.4 Collecting Data -- 4.4.1 Data Management Infrastructure -- 4.4.2 Monitoring and Validation -- 4.4.3 Metadata -- 4.4.4 Security -- 4.4.4.1 Access Policy -- 4.4.4.2 Protecting Research Data -- 4.5 Analysing Data -- 4.5.1 Raw Data Preparation -- 4.5.2 Analysis Plan -- 4.6 Archiving Data -- 4.6.1 Archiving: What and How? -- 4.6.2 Archiving: Where? -- 4.7 Sharing Data -- 4.7.1 General Considerations -- 4.7.1.1 Anonymity -- 4.7.2 Sharing with Commercial Parties -- 4.8 Conclusion -- References -- Chapter 5: The EU's General Data Protection Regulation (GDPR) in a Research Context -- 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 -- Part II: From Data to Model -- Chapter 6: Preparing Data for Predictive Modelling -- 6.1 Introduction -- 6.2 Study Designs for Prediction Model Development -- 6.2.1 Retrospective and Prospective Data -- 6.2.2 Alternative Study Designs -- 6.2.3 Patient Selection -- 6.3 Sample Size Considerations -- 6.3.1 Potential Predictor Variables and Model Overfitting -- 6.3.2 Sample Size Rules-of-thumb -- 6.4 Pre-processing Your Data -- 6.4.1 Transforming Predictor Variables -- 6.4.2 Categorizing Predictor Variables -- 6.4.3 Visualizing Data -- 6.5 Missing Data -- 6.5.1 Why You Should Bother About Missing Data -- 6.5.2 Handling Missing Data -- References.
,
Chapter 7: Extracting Features from Time Series -- 7.1 Time-Domain Processing -- 7.1.1 Basic Magnitude Features and Time-Locked Averaging -- 7.1.2 Template Matching -- 7.1.3 Weighted Moving Averages: Frequency Filtering -- 7.1.3.1 Weighted Moving Averages with Feedback -- 7.2 Frequency-Domain Processing -- 7.2.1 Band Power -- 7.2.2 Spectral Analysis -- 7.2.2.1 Fast Fourier Transform (FFT) -- 7.2.2.2 Windowing -- 7.2.2.3 Autoregressive (AR) Modeling -- 7.3 Time-Frequency Processing: Wavelets -- 7.4 Conclusion -- References -- Chapter 8: Prediction Modeling Methodology -- 8.1 Statistical Hypothesis Testing -- 8.1.1 Types of Error -- 8.2 Creating a Prediction Model Using Regression Techniques -- 8.2.1 Prediction Modeling Using Linear and Logistic Regression -- 8.2.2 Software and Courses for Prediction Modeling -- 8.2.3 A Short Word on Modeling Time-to-Event Outcomes -- 8.3 Creating a Model That Performs Well Outside the Training Set -- 8.3.1 The Bias-Variance Tradeoff -- 8.3.2 Techniques for Making a General Model -- 8.4 Model Performance Metrics -- 8.4.1 General Performance Metrics -- 8.4.2 Confusion Matrix -- 8.4.3 Performance Metrics Derived from the Confusion Matrix -- 8.4.4 Model Discrimination: Receiver Operating Characteristic and Area Under the Curve -- 8.4.5 Model Calibration -- 8.5 Validation of a Prediction Model -- 8.5.1 The Importance of Splitting Training/Test Sets -- 8.5.2 Techniques for Internal Validation -- 8.5.3 External Validation -- 8.6 Summary Remarks -- 8.6.1 What Has Been Learnt -- 8.6.2 Further Reading -- References -- Chapter 9: Diving Deeper into Models -- 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.
,
9.7.1 Support Vector Machines (SVMs) -- 9.7.2 Random Forests (RF) -- 9.7.3 Artificial Neural Networks (ANNs) -- 9.8 Unsupervised Algorithms -- 9.8.1 K-means -- 9.8.2 Hierarchical Clustering -- 9.9 Conclusion -- References -- Chapter 10: Reporting Standards and Critical Appraisal of Prediction Models -- 10.1 Introduction -- 10.1.1 Chapter Overview -- 10.2 Prediction Modelling Studies -- 10.2.1 Development -- 10.2.2 Validation -- 10.2.3 Updates -- 10.2.4 Impact Assessment and Clinical Implementation -- 10.3 Reporting Your Own Work -- 10.3.1 Purpose of Transparent Reporting Guidelines -- 10.3.2 Context -- 10.3.3 Sample Size, Predictors and Predictor Selection -- 10.3.4 Missing Data -- 10.3.5 Model Specification and Predictive Performance -- 10.3.6 Model Presentation, Ease of Interpretation and Intended Impact -- 10.4 Critical Appraisal of Published Models -- 10.4.1 Relevant Context of Prediction Modelling Studies -- 10.4.2 Applicability and Risk of Bias -- 10.4.3 Systematic Reviews and Meta-analyses -- 10.5 Conclusion -- References -- Part III: From Model to Application -- Chapter 11: Clinical Decision Support Systems -- 11.1 Introduction on CDSS -- 11.1.1 What Is CDSS? -- 11.1.2 Why CDSS? -- 11.1.3 Types of CDSS -- 11.1.4 Medication Related CDSS -- 11.2 Challenges for Implementing a CDSS -- 11.2.1 High Adoption and Effective Use -- 11.2.1.1 Alert Fatigue -- 11.2.1.2 Triggers -- 11.2.1.3 Context Factors -- 11.2.2 Best Knowledge Available when Needed -- 11.2.2.1 When Needed: Integration in Clinical Workflow -- 11.2.2.2 Knowledge Is Available -- 11.3 Best Knowledge & -- Continuous Improvement of Knowledge and CDSS Methods -- 11.3.1 CDSS Verification and Validation -- 11.3.2 Development and Validation Strategy -- 11.3.2.1 Strategy for Development and Validation of Clinical Rules -- Step 1: Technical Validation.
,
Step 2: Therapeutic Retrospective Validation -- Step 3: Pre-implementation Prospective Validation -- Step 4: Post-implementation Prospective Validation -- 11.3.2.2 Adaption in Practice -- 11.4 Future Perspectives -- References -- Chapter 12: Mobile Apps -- 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 -- Chapter 13: Optimizing Care Processes with Operational Excellence & -- Process Mining -- 13.1 Introduction -- 13.2 Care Process -- 13.3 Operational Excellence -- 13.3.1 Lean Thinking -- 13.3.2 Six Sigma -- 13.3.3 Lean Six Sigma -- 13.4 Process Mining -- 13.5 Sociotechnical Systems & -- Leadership -- 13.5.1 Sociotechnical Systems -- 13.5.2 Leadership -- 13.6 Conclusion -- References -- Chapter 14: Value-Based Health Care Supported by Data Science -- 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 -- Index.
Additional Edition:
Print version: Kubben, Pieter Fundamentals of Clinical Data Science Cham : Springer International Publishing AG,c2019 ISBN 9783319997124
Language:
English
Keywords:
Electronic books.
;
Electronic books
;
Electronic books
URL:
FULL
((OIS Credentials Required))
URL:
FULL
((OIS Credentials Required))
URL:
https://doi.org/10.1007/978-3-319-99713-1
Bookmarklink
