Big data in healthcare : extracting knowledge from point-of-care machines /: extracting knowledge from point-of-care machines. ([2017])
- Record Type:
- Book
- Title:
- Big data in healthcare : extracting knowledge from point-of-care machines /: extracting knowledge from point-of-care machines. ([2017])
- Main Title:
- Big data in healthcare : extracting knowledge from point-of-care machines
- Further Information:
- Note: Pouria Amirian, Trudie Lang, Francois van Loggerenberg, editors.
- Editors:
- Amirian, Pouria
Lang, Trudie
Van Loggerenberg, Francois - 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 andAbout 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. … (more)
- Publisher Details:
- Cham, Switzerland : Springer
- Publication Date:
- 2017
- Copyright Date:
- 2017
- Extent:
- 1 online resource (vii, 100 pages), illustrations
- Subjects:
- 610.285
Medical informatics
Big data
Data mining
Medicine -- Data processing
HEALTH & FITNESS -- Holism
HEALTH & FITNESS -- Reference
MEDICAL -- Alternative Medicine
MEDICAL -- Atlases
MEDICAL -- Essays
MEDICAL -- Family & General Practice
MEDICAL -- Holistic Medicine
MEDICAL -- Osteopathy
Big data
Data mining
Medical informatics
Medicine -- Data processing
Medical Informatics -- methods
Health Information Systems
Public Health Surveillance -- methods
Electronic book
Electronic books - Languages:
- English
- ISBNs:
- 9783319629902
3319629905 - Related ISBNs:
- 3319629883
9783319629889 - Notes:
- Note: Includes bibliographical references and index.
Note: Online resource; title from PDF title page (EBSCO, viewed October 3, 2017). - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.396811
- Ingest File:
- 02_424.xml