<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Roth JA</dc:creator>
  <dc:creator>Battegay M</dc:creator>
  <dc:creator>Juchler F</dc:creator>
  <dc:creator>Vogt JE</dc:creator>
  <dc:creator>Widmer AF</dc:creator>
  <dc:date>2018</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">To exploit the full potential of big routine data in healthcare and to efficiently communicate and collaborate with information technology specialists and data analysts, healthcare epidemiologists should have some knowledge of large-scale analysis techniques, particularly about machine learning. This review focuses on the broad area of machine learning and its first applications in the emerging field of digital healthcare epidemiology.</dc:description>
  <dc:identifier>https://sonar.ch/global/documents/232425</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1017/ice.2018.265</dc:relation>
  <dc:relation>info:eu-repo/semantics/altIdentifier/pmid/30394238</dc:relation>
  <dc:source>Infection control and hospital epidemiology. - 2018</dc:source>
  <dc:subject xmlns:ns1="xml" ns1:lang="en">Big Data</dc:subject>
  <dc:subject xmlns:ns2="xml" ns2:lang="en">Biomedical Research</dc:subject>
  <dc:subject xmlns:ns3="xml" ns3:lang="en">Electronic Health Records</dc:subject>
  <dc:subject xmlns:ns4="xml" ns4:lang="en">Epidemiologic Studies</dc:subject>
  <dc:subject xmlns:ns5="xml" ns5:lang="en">Humans</dc:subject>
  <dc:subject xmlns:ns6="xml" ns6:lang="en">Machine Learning</dc:subject>
  <dc:title xmlns:ns7="xml" ns7:lang="en">Introduction to Machine Learning in Digital Healthcare Epidemiology.</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>