Med bibl SkaS : Big data in healthcare : extracting knowledge from point-of-care machines

Träfflista

Big data in healthcare : extracting knowledge from point-of-care machines

Titel och upphov	Big data in healthcare : extracting knowledge from point-of-care machines
Utgivning, distribution etc.	Cham, Switzerland : Springer, [2017] ©2017
National Library of Medicine (NLM) klassifikationskod	2018 C-183 W 26.5
DDC klassifikationskod (Dewey Decimal Classification)	610.285
Fysisk beskrivning	1 online resource (vii, 100 pages) : illustrations.
Serietitel - ej biuppslagsform	SpringerBriefs in pharmaceutical science & drug development.
Anmärkning: Bibliografi etc.	Includes bibliographical references and index.
Anmärkning: Innehåll	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.
Anmärkning: Innehållsbeskrivning, sammanfattning	"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."-- Provided by publisher.
Term	Medical Informatics. Big data. Data Mining. Medicine HEALTH & FITNESS HEALTH & FITNESS MEDICAL MEDICAL MEDICAL MEDICAL MEDICAL MEDICAL Big data. Data mining. Medical informatics. Medicine Biomedicine. Pharmaceutical Sciences/Technology. Health Informatics. Medical Informatics Health Information Systems. Public Health Surveillance
Genre/Form	Electronic books. Electronic book.
Personnamn	Amirian, Pouria, Lang, Trudie, Van Loggerenberg, Francois,
Annat medium	Print version: Big data in healthcare. Cham, Switzerland : Springer, [2017] ISBN 3319629883 ISBN 9783319629889
Seriebiuppslag under titel	SpringerBriefs in pharmaceutical science & drug development.
Elektronisk adress och åtkomst (URI)	http://link.springer.com/10.1007/978-3-319-62990-2
ISBN	9783319629902 3319629905

*00000000cam a2200000Ii 4500
*00179626
*00520181229023856.7
*006m     o  d
*007cr |n|||||||||
*008170922s2017    sz a    ob    001 0 eng d
*019  $a1004423961$a1004733729$a1008870826$a1011956618$a1048139400$a1058541811$a1066632588$a1066633315
*020  $a9783319629902$q(electronic bk.)
*020  $a3319629905$q(electronic bk.)
*020  $z3319629883
*020  $z9783319629889
*0247 $a10.1007/978-3-319-62990-2$2doi
*029  $aGBVCP$b1000347311
*035  $a(OCoLC)1004597944$z(OCoLC)1004423961$z(OCoLC)1004733729$z(OCoLC)1008870826$z(OCoLC)1011956618$z(OCoLC)1048139400$z(OCoLC)1058541811$z(OCoLC)1066632588$z(OCoLC)1066633315
*035  $a(OCoLC)on1004597944
*040  $aYDX$beng$erda$epn$cYDX$dN$dEBLCP$dN$dGW5XE$dAZU$dUPM$dOCLCF$dCOO$dMERUC$dCAUOI$dOCLCQ$dIOG$dKSU$dAHH$dVT2$dU3W$dOCLCO$dGZM$dOCLCO$dAU@$dOCLCQ$dWYU$dOCLCO$dOCLCQ$dOCLCA$dLVT$dCASUM$dOCLCO
*049  $aMAIN
*050 4$aR858
*06000$a2018 C-183
*06010$aW 26.5
*072 7$aHEA$x012000$2bisacsh
*072 7$aHEA$x020000$2bisacsh
*072 7$aMED$x004000$2bisacsh
*072 7$aMED$x101000$2bisacsh
*072 7$aMED$x109000$2bisacsh
*072 7$aMED$x029000$2bisacsh
*072 7$aMED$x040000$2bisacsh
*072 7$aMED$x092000$2bisacsh
*072 7$aTDCW$2bicssc
*08204$a610.285$223
*24500$aBig data in healthcare :$bextracting knowledge from point-of-care machines /$cPouria Amirian, Trudie Lang, Francois van Loggerenberg, editors.
*264 1$aCham, Switzerland :$bSpringer,$c[2017]
*264 4$c©2017
*300  $a1 online resource (vii, 100 pages) :$billustrations.
*336  $atext$btxt$2rdacontent
*337  $acomputer$bc$2rdamedia
*338  $aonline resource$bcr$2rdacarrier
*347  $atext file$bPDF$2rda
*4901 $aSpringerBriefs in pharmaceutical science & drug development
*504  $aIncludes bibliographical references and index.
*5050 $aAbout 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.
*5058 $a2.3 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.
*5058 $a3.2.3 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.
*5058 $a3.7 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.
*5058 $a5 #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.
*520  $a"This book reviews a number of issues including: Why data generated from POC machines are considered as Big Data. What are the challenges in storing, managing, extracting knowledge from data from POC devices? Why is it inefficient to use traditional data analysis with big data? What are the solutions for the mentioned issues and challenges? What type of analytics skills are required in health care? What big data technologies and tools can be used efficiently with data generated from POC devices? This book shows how it is feasible to store vast numbers of anonymous data and ask highly specific questions that can be performed in real-time to give precise and meaningful evidence to guide public health policy."--$cProvided by publisher.
*5880 $aOnline resource; title from PDF title page (EBSCO, viewed October 3, 2017).
*590  $aSpringerLink$bSpringer Medicine eBooks 2017 English+International
*650 0$aMedical Informatics.
*650 0$aBig data.
*650 0$aData Mining.
*650 0$aMedicine$xData processing.
*650 7$aHEALTH & FITNESS$xHolism.$2bisacsh
*650 7$aHEALTH & FITNESS$xReference.$2bisacsh
*650 7$aMEDICAL$xAlternative Medicine.$2bisacsh
*650 7$aMEDICAL$xAtlases.$2bisacsh
*650 7$aMEDICAL$xEssays.$2bisacsh
*650 7$aMEDICAL$xFamily & General Practice.$2bisacsh
*650 7$aMEDICAL$xHolistic Medicine.$2bisacsh
*650 7$aMEDICAL$xOsteopathy.$2bisacsh
*650 7$aBig data.$2fast$0(OCoLC)fst01892965
*650 7$aData mining.$2fast$0(OCoLC)fst00887946
*650 7$aMedical informatics.$2fast$0(OCoLC)fst01014175
*650 7$aMedicine$xData processing.$2fast$0(OCoLC)fst01014924
*65014$aBiomedicine.
*65024$aPharmaceutical Sciences/Technology.
*65024$aHealth Informatics.
*65012$aMedical Informatics$xmethods.
*65022$aHealth Information Systems.
*65022$aPublic Health Surveillance$xmethods.
*655 4$aElectronic books.
*655 0$aElectronic book.
*7001 $aAmirian, Pouria,$eeditor.
*7001 $aLang, Trudie,$eeditor.
*7001 $aVan Loggerenberg, Francois,$eeditor.
*77608$iPrint version:$tBig data in healthcare.$dCham, Switzerland : Springer, [2017]$z3319629883$z9783319629889$wIMP(OCoLC)(OCoLC)991689001
*830 0$aSpringerBriefs in pharmaceutical science & drug development.
*85640$uhttp://link.springer.com/10.1007/978-3-319-62990-2
*938  $aEBL - Ebook Library$bEBLB$nEBL5049860
*938  $aEBSCOhost$bEBSC$n1595551
*938  $aYBP Library Services$bYANK$n14810941
*994  $a92$bSESKS
^

Det finns inga omdömen till denna titeln.
Klicka här för att vara den första som skriver ett omdöme.

Dokument

http://link.springer.com/10.1007/978-3-319-62990-2

Sänd till