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

Bruzelius, Emilie; Le, Matthew; Kenny, Avi; Downey, Jordan; Danieletto, Matteo; Baum, Aaron; Doupe, Patrick; Silva, Bruno; Landrigan, Philip J; Singh, Prabhjot

Bruzelius, Emilie; Le, Matthew; Kenny, Avi; Downey, Jordan; Danieletto, Matteo; Baum, Aaron; Doupe, Patrick; Silva, Bruno; Landrigan, Philip J; Singh, Prabhjot.

Afiliación

Bruzelius E; Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Le M; Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Kenny A; Last Mile Health, Congo Town, Monrovia, Liberia.
Downey J; Department of Biostatistics, University of Washington, Seattle, Washington, USA.
Danieletto M; Last Mile Health, Congo Town, Monrovia, Liberia.
Baum A; Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Doupe P; Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Silva B; Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Landrigan PJ; Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Singh P; Schiller Institute for Integrated Science and Society, Boston College, Chestnut Hill, Massachusetts, USA.

J Am Med Inform Assoc ; 26(8-9): 806-812, 2019 08 01.

Article en En | MEDLINE | ID: mdl-31411691

Asunto(s)

Aprendizaje Profundo; Accesibilidad a los Servicios de Salud; Servicios de Salud Rural; Imágenes Satelitales; Algoritmos; Agentes Comunitarios de Salud; Geografía Médica; Humanos; Población Rural

Palabras clave

community health workers; deep learning; global health; neural networks; public health surveillance

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Servicios de Salud Rural / Imágenes Satelitales / Aprendizaje Profundo / Accesibilidad a los Servicios de Salud Tipo de estudio: Prognostic_studies Aspecto: Determinantes_sociais_saude Límite: Humans Idioma: En Revista: J Am Med Inform Assoc Asunto de la revista: INFORMATICA MEDICA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

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