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Big Data in Healthcare [electronic resource] : Extracting Knowledge from Point-of-Care Machines / edited by Pouria Amirian, Trudie Lang, Francois van Loggerenberg.

Medverkande: Materialtyp: TextSerie: SpringerBriefs in Pharmaceutical Science & Drug DevelopmentUtgivningsuppgift: Cham : Springer International Publishing : Imprint: Springer, 2017Beskrivning: VII, 100 p. 46 illus., 45 illus. in color. online resourceInnehållstyp:
  • text
Medietyp:
  • computer
Bärartyp:
  • online resource
ISBN:
  • 9783319629902
Ämnen: Fler format: Printed edition:: Ingen titelDDK-klassifikation:
  • 615.19 23
Library of Congress (LC) klassifikationskod:
  • RS380
  • RS190-210
Onlineresurser:
Innehåll:
Introduction – Improving Healthcare with Big Data -- Data Science and Analytics -- Big Data and Big Data Technologies -- Big Data Analytics for Extracting Disease Surveillance Information: An Untapped Opportunity -- Ebola and Twitter. What Insights Can Public Health Draw from Social Media?
I: Springer eBooksSammanfattning: 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.
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Introduction – Improving Healthcare with Big Data -- Data Science and Analytics -- Big Data and Big Data Technologies -- Big Data Analytics for Extracting Disease Surveillance Information: An Untapped Opportunity -- Ebola and Twitter. What Insights Can Public Health Draw from Social Media?

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.

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