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Machine learning in population health: Opportunities and threats

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  • Abraham D Flaxman
  • Theo Vos

Abstract

Abraham D. Flaxman and Theo Vos of the Institute for Health Metrics and Evaluation, University of Washington, discuss near-term applications for ML in population health and name their priorities for ongoing ML development.

Suggested Citation

  • Abraham D Flaxman & Theo Vos, 2018. "Machine learning in population health: Opportunities and threats," PLOS Medicine, Public Library of Science, vol. 15(11), pages 1-3, November.
    • Handle: RePEc:plo:pmed00:1002702
    DOI: 10.1371/journal.pmed.1002702
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    References listed on IDEAS

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    1. Erin K Nichols & Peter Byass & Daniel Chandramohan & Samuel J Clark & Abraham D Flaxman & Robert Jakob & Jordana Leitao & Nicolas Maire & Chalapati Rao & Ian Riley & Philip W Setel & on behalf of the , 2018. "The WHO 2016 verbal autopsy instrument: An international standard suitable for automated analysis by InterVA, InSilicoVA, and Tariff 2.0," PLOS Medicine, Public Library of Science, vol. 15(1), pages 1-9, January.
    2. Gretchen A Stevens & Leontine Alkema & Robert E Black & J Ties Boerma & Gary S Collins & Majid Ezzati & John T Grove & Daniel R Hogan & Margaret C Hogan & Richard Horton & Joy E Lawn & Ana Marušić & C, 2016. "Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement," PLOS Medicine, Public Library of Science, vol. 13(6), pages 1-8, June.
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