Big data in healthcare [electronic resource] : extracting knowledge from point-of-care machines / Pouria Amirian, Trudie Lang, Francois van Loggerenberg, editors.

Cham, Switzerland : Springer, [2017]
SpringerBriefs in pharmaceutical science & drug development
SpringerBriefs in pharmaceutical science & drug development
1 online resource.
Medical informatics.
Big data.
Data mining.
Medicine -- Data processing.
Medical subjects:
Medical Informatics.
About the Editors; 1 Introduction-Improving Healthcare with Big Data; 1.1 Introduction; 1.2 Big Data and Health; 1.3 Big Data and Health in Low- and Middle-Income Countries; 1.3.1 Analytical Challenges; 1.3.2 Ethical Challenges; Informed Consent; Privacy; Ownership; Epistemology and Objectivity; Big Data 'Divides'; 1.4 Conclusion and Structure of the Book; References; 2 Data Science and Analytics; 2.1 What Is Data Science?; 2.2 Methods in Data Science; 2.2.1 Supervised and Unsupervised Learning; 2.2.2 Data Science Analytical Tasks
2.3 Data Science, Analytics, Statistics, Business Intelligence and Data Mining2.3.1 Data Science and Analytics; 2.3.2 Statistics, Statistical Learning and Data Science; 2.3.3 Data Science and Business Intelligence; 2.4 Data Science Process; 2.4.1 CRISP-DM; 2.4.2 Domain Knowledge and Business Understanding; 2.4.3 Data Understanding and Preparation; 2.4.4 Building Models and Evaluation Metrics; 2.4.5 Model Deployment; 2.5 Data Science Tools; 2.6 Summary; References; 3 Big Data and Big Data Technologies; 3.1 What Is Big Data?; 3.2 Data Dimension of Big Data; 3.2.1 Volume; 3.2.2 Velocity
3.2.3 Variety3.2.4 Other Vs of Big Datasets; 3.3 Structured, Unstructured and Semi-structured Data; 3.3.1 Internet of Things and Machine-Generated Data; 3.3.2 Highly Connected Data; 3.4 Big Data Technologies; 3.4.1 Building Blocks of Hadoop: HDFS and MapReduce; 3.4.2 Distributed Processing with MapReduce; 3.4.3 HDFS and MapReduce; 3.4.4 Hadoop Ecosystem: First Generation; 3.4.5 Hadoop Ecosystem Second Generation; 3.5 Splunk: A Commercial Big Data Technology; 3.6 Big Data Pipeline: Lambda and Kappa Architectures; 3.6.1 Lambda Architecture; 3.6.2 Kappa Architecture
3.7 Big Data Tools and TechnologiesReferences; 4 Big Data Analytics for Extracting Disease Surveillance Information: An Untapped Opportunity; 4.1 Introduction; 4.2 The Importance of POC; 4.3 Technical Requirements of POC; 4.4 Data Generated by POC and Accessibility Issue; 4.5 Proposed Solution; 4.5.1 Common Data Structure of the Proposed Solution; 4.5.2 Data Analytics in the Proposed Solution; 4.6 Big Data Architecture of the Proposed Solution; 4.7 Benefits of the Implemented System; 4.8 The Implemented Data Analytics and Dashboards; 4.9 Conclusions and Future Work; References
5 #Ebola and Twitter. What Insights Can Global Health Draw from Social Media?5.1 Introduction; 5.2 Ebola Virus Disease and Media Coverage; 5.3 How Can We Study Social Media Data?; 5.4 Insights from the Ebola Twitter Dataset; 5.5 Conclusion; Acknowledgements; References; Index
Electronic reproduction. Ann Arbor, MI Available via World Wide Web.
Online resource; title from PDF title page (EBSCO, viewed October 3, 2017).
Includes bibliographical references and index.
Local notes:
Acquired for the Penn Libraries with assistance from the School of Medicine Library Fund.
Amirian, Pouria, editor.
Lang, Trudie, editor.
Van Loggerenberg, Francois, editor.
ProQuest ebook central
School of Medicine Library Fund.
Publisher Number:
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Restricted for use by site license.
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