Table of Contents: Fundamentals of Clinical Data Science.

Fundamentals of Clinical Data Science.

Bibliographic Details
Main Author:	Kubben, Pieter.
Other Authors:	Dumontier, Michel., Dekker, Andre.
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing AG, 2019.
Edition:	1st ed.
Subjects:	Electronic books.
Online Access:	Click to View

Table of Contents:

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 &amp
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 &amp
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 &amp
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.

Fundamentals of Clinical Data Science.

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