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Machine learning to refine decision making within a syndromic surveillance service.
Lake, I R; Colón-González, F J; Barker, G C; Morbey, R A; Smith, G E; Elliot, A J.
Affiliation
  • Lake IR; School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK. I.Lake@uea.ac.uk.
  • Colón-González FJ; National Institute for Health Research Health Protection Research Unit in Emergency Preparedness and Response, London, UK. I.Lake@uea.ac.uk.
  • Barker GC; School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.
  • Morbey RA; National Institute for Health Research Health Protection Research Unit in Emergency Preparedness and Response, London, UK.
  • Smith GE; National Institute for Health Research Health Protection Research Unit in Emergency Preparedness and Response, London, UK.
  • Elliot AJ; National Institute for Health Research Health Protection Research Unit in Emergency Preparedness and Response, London, UK.
BMC Public Health ; 19(1): 559, 2019 May 14.
Article in En | MEDLINE | ID: mdl-31088446
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Full text: 1 Database: MEDLINE Main subject: Public Health / Sentinel Surveillance / Risk Assessment / Decision Making / Machine Learning Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limits: Humans Country/Region as subject: Europa Language: En Year: 2019 Type: Article
Fulltext
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PubMed Links
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Full text: 1 Database: MEDLINE Main subject: Public Health / Sentinel Surveillance / Risk Assessment / Decision Making / Machine Learning Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limits: Humans Country/Region as subject: Europa Language: En Year: 2019 Type: Article