The Resource Big data in healthcare : extracting knowledge from point-of-care machines, Pouria Amirian, Trudie Lang, Francois van Loggerenberg, editors
Big data in healthcare : extracting knowledge from point-of-care machines, Pouria Amirian, Trudie Lang, Francois van Loggerenberg, editors
Resource Information
The item Big data in healthcare : extracting knowledge from point-of-care machines, Pouria Amirian, Trudie Lang, Francois van Loggerenberg, editors represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item Big data in healthcare : extracting knowledge from point-of-care machines, Pouria Amirian, Trudie Lang, Francois van Loggerenberg, editors represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.
This item is available to borrow from 1 library branch.
- Summary
- "This book reviews a number of issues including: Why data generated from POC machines are considered as Big Data. What are the challenges in storing, managing, extracting knowledge from data from POC devices? Why is it inefficient to use traditional data analysis with big data? What are the solutions for the mentioned issues and challenges? What type of analytics skills are required in health care? What big data technologies and tools can be used efficiently with data generated from POC devices? This book shows how it is feasible to store vast numbers of anonymous data and ask highly specific questions that can be performed in real-time to give precise and meaningful evidence to guide public health policy."--
- Language
- eng
- Extent
- 1 online resource.
- 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
- Isbn
- 9783319629889
- Label
- Big data in healthcare : extracting knowledge from point-of-care machines
- Title
- Big data in healthcare
- Title remainder
- extracting knowledge from point-of-care machines
- Statement of responsibility
- Pouria Amirian, Trudie Lang, Francois van Loggerenberg, editors
- Language
- eng
- Summary
- "This book reviews a number of issues including: Why data generated from POC machines are considered as Big Data. What are the challenges in storing, managing, extracting knowledge from data from POC devices? Why is it inefficient to use traditional data analysis with big data? What are the solutions for the mentioned issues and challenges? What type of analytics skills are required in health care? What big data technologies and tools can be used efficiently with data generated from POC devices? This book shows how it is feasible to store vast numbers of anonymous data and ask highly specific questions that can be performed in real-time to give precise and meaningful evidence to guide public health policy."--
- Assigning source
- Provided by publisher
- Cataloging source
- YDX
- Dewey number
- 610.285
- Index
- index present
- LC call number
- R858
- Literary form
- non fiction
- Nature of contents
-
- dictionaries
- bibliography
- http://library.link/vocab/relatedWorkOrContributorName
-
- Amirian, Pouria
- Lang, Trudie
- Van Loggerenberg, Francois
- Series statement
- SpringerBriefs in pharmaceutical science & drug development
- http://library.link/vocab/subjectName
-
- Medical informatics
- Big data
- Data mining
- Medicine
- Label
- Big data in healthcare : extracting knowledge from point-of-care machines, Pouria Amirian, Trudie Lang, Francois van Loggerenberg, editors
- Bibliography note
- Includes bibliographical references and index
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- 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
- Control code
- SPR1004597944
- Dimensions
- unknown
- Extent
- 1 online resource.
- Form of item
- online
- Isbn
- 9783319629889
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
- 10.1007/978-3-319-62990-2
- Specific material designation
- remote
- System control number
-
- on1004597944
- (OCoLC)1004597944
- Label
- Big data in healthcare : extracting knowledge from point-of-care machines, Pouria Amirian, Trudie Lang, Francois van Loggerenberg, editors
- Bibliography note
- Includes bibliographical references and index
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- 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
- Control code
- SPR1004597944
- Dimensions
- unknown
- Extent
- 1 online resource.
- Form of item
- online
- Isbn
- 9783319629889
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
- 10.1007/978-3-319-62990-2
- Specific material designation
- remote
- System control number
-
- on1004597944
- (OCoLC)1004597944
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.liverpool.ac.uk/portal/Big-data-in-healthcare--extracting-knowledge/_iKtTAFajQ0/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.liverpool.ac.uk/portal/Big-data-in-healthcare--extracting-knowledge/_iKtTAFajQ0/">Big data in healthcare : extracting knowledge from point-of-care machines, Pouria Amirian, Trudie Lang, Francois van Loggerenberg, editors</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.liverpool.ac.uk/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.liverpool.ac.uk/">Sydney Jones Library, University of Liverpool</a></span></span></span></span></div>
