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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

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ISBN: 0585052840 , 9780585052847

Series Statement: EBSCOhost eBook Collection

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ISBN: 0585048851 , 9780585048857

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Series Statement: Münchener Beiträge zur romanischen und englischen Philologie 21

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Subjects: English Studies

Keywords: Hochschulschrift

Author information: Dekker, Thomas, 1572-1632.

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Format: VII, 149 S. : , Ill.

Edition: 1. publ

ISBN: 0-7190-5247-5

Series Statement: Revels student editions

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Subjects: English Studies

Keywords: The witch of Edmonton ; Hochschulschrift ; Kommentar ; Hochschulschrift ; Kommentar

Author information: Ford, John 1586-1639

Author information: Dekker, Thomas 1572-1632

Author information: Rowley, William 1585-1642

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Format: Online-Ressource (212 p) , col., ill., strated inthroughout

Edition: RSC eBook Collection 1968-2009

ISBN: 1847558909 , 9781847558909

Series Statement: RSC Nanoscience & nanotechnology v. 6

Content: Accessible in style, Nanofluidics fills a gap in the literature for a book focusing on bioanalytical applications within this growing field, In his now celebrated lecture at the 1959 meeting of the American Physical Society, Richard Feynman pondered the potential of miniaturization in the physical sciences. His vision, based on known technology, examined the limits set by physical principles and proposed a variety of new nano-tools including the concept of "atom-by-atom" fabrication. In the intervening decades, many of these predictions have become reality. In particular, the development and application of nanofluidics is becoming a competitive and exciting field of research. These nanoscale analytical instruments employ micromachined features and are able to manipulate fluid samples with high precision and efficiency. In a fundamental sense, chip-based analytical systems have been shown to have many advantages over their conventional (larger) analogues. Despite the growth of this field, there are surprisingly few books dedicated to nanofluidics. This book will fill the gap in the literature for a text focusing on bioanalytical applications. Written at a level accessible to experts and non-experts alike, it has the potential to become a mainstream text book for advanced nanobiotechnology courses within academic institutions

Note: Ebook , Nanofluidics_publicity; full book resized , Chapter 1: Transport of Ions, DNA Polymers, and Microtubules in the Nanofluidic Regime-- Chapter 2: Biomolecule Separation, Concentration, and Detection using Nanofluidic Channels-- Chapter 3: Particle Transport in Micro and Nanostructured Arrays: Asymmetric Low Reynolds Number Flow-- Chapter 5: Fabrication of Silica Nanofluidic Tubing for Single Molecule Detection-- Chapter 6: Single Molecule Analysis Using Single Nanopores-- Chapter 7: Nanopore-Based Optofluidic Devices for Single Molecule Sensing-- Chapter 8: Ion-Current Rectification in Nanofluidic Devices-- Chapter 9: Nanopillars and Nanoballs for DNA Analysis--.

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Format: 1 Online-Ressource (372 Seiten) , Diagramme

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ISBN: 9783653019711 , 3653019710 , 9783631628348 , 363162834X

Series Statement: Labour, education & society 32

Content: This book examines the state of print and electronic media in the United States of America, Europe, and China. The latest mass communication advances demonstrate that we live in an increasingly media-centric world. The chapters include theoretical and empirical studies that shed light on the meaning of this development. The trajectory of people?s move to electronic communication is a global phenomenon affecting their daily life. Does this trend aid or impede democracy? Is there an emerging digital divide contributing to an increasing gap between the rich and poor people and nations? The four parts of this book explore various aspects of political socialization and its relationship with different media, including print, broadcasting, and the Internet

Content: Introduction -- Political socialization defined: setting the context -- Youth, peer culture, and everyday political consciousness -- Politics, education, and paradigmatic reconceptualism -- Media use in the United States -- Participation friendliness of political websites -- Empirical evaluation of government and websites -- The internet upholds the powers that be -- Metaphors in Euroland press -- Press reporting on the euro -- The Ukraine media on the orange revolution -- Post-communist media in Russia -- Media and terrorists -- Democracy and virtual politics -- The electronic media deficit -- Does the media reduce political participation? -- Implications for e-media, the press, government, and politics in China

Note: Includes bibliographical references and indexes , Also issued in print and PDF version. , In English

Additional Edition: ISBN 9783631628348

Additional Edition: Erscheint auch als Druck-Ausgabe E-political socialization, the press and politics Frankfurt am Main : PL Acad. Research, 2014 ISBN 363162834X

Additional Edition: ISBN 9783631628348

Language: English

Subjects: Political Science , Sociology

Keywords: USA ; Europa ; China ; Massenmedien ; Neue Medien ; Politische Sozialisation ; Electronic books ; Aufsatzsammlung

URL: View this content on Open Research Library

URL: http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf

Author information: Sünker, Heinz 1948-

Author information: Farnen, Russell Francis 1933-