Satellite images and machine learning can identify remote communities to facilitate access to health services.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 31411691

Abstract

OBJECTIVE: Community health systems operating in remote areas require accurate information about where people live to efficiently provide services across large regions. We sought to determine whether a machine learning analyses of satellite imagery can be used to map remote communities to facilitate service delivery and planning.

Authors

  • Emilie Bruzelius
    Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Matthew Le
    Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Avi Kenny
    Last Mile Health, Congo Town, Monrovia, Liberia.
  • Jordan Downey
    Last Mile Health, Congo Town, Monrovia, Liberia.
  • Matteo Danieletto
    1Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY USA.
  • Aaron Baum
    Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Patrick Doupe
    Zalando SE, Berlin, Germany. Electronic address: patrick.doupe@zalando.de.
  • Bruno Silva
    Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Philip J Landrigan
    Schiller Institute for Integrated Science and Society, Boston College, Chestnut Hill, Massachusetts, USA.
  • Prabhjot Singh
    Cleveland Clinic Florida, Department of Urology, Weston, FL, USA.

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