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Big data in healthcare : extracting knowledge from point-of-care machines / Pouria Amirian, Trudie Lang, Francois van Loggerenberg, editors.
- Format:
- Book
- Series:
- SpringerBriefs in pharmaceutical science & drug development
- Language:
- English
- Subjects (All):
- Medical Informatics.
- Medical informatics.
- Big data.
- Data mining.
- Medicine--Data processing.
- Medicine.
- Medical Subjects:
- Medical Informatics.
- Physical Description:
- 1 online resource.
- Place of Publication:
- Cham, Switzerland : Springer, [2017]
- System Details:
- text file
- Contents:
- 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; 1.3.2.1 Informed Consent; 1.3.2.2 Privacy; 1.3.2.3 Ownership; 1.3.2.4 Epistemology and Objectivity; 1.3.2.5 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
- Notes:
- 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.
- ISBN:
- 9783319629902
- 3319629905
- Publisher Number:
- 99973924465
- Access Restriction:
- Restricted for use by site license.
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