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Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol

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dc.contributor.author Carrillo-Larco, Rodrigo M.
dc.contributor.author Tudor Car, Lorainne
dc.contributor.author Pearson-Stuttard, Jonathan
dc.contributor.author Panch, Trishan
dc.contributor.author Miranda, J. Jaime
dc.contributor.author Atun, Rifat
dc.date.accessioned 2020-07-14T00:01:02Z
dc.date.available 2020-07-14T00:01:02Z
dc.date.issued 2020
dc.identifier.uri https://hdl.handle.net/20.500.12866/8255
dc.description.abstract Introduction: Machine learning (ML) has been used in bio-medical research, and recently in clinical and public health research. However, much of the available evidence comes from high-income countries, where different health profiles challenge the application of this research to low/middle-income countries (LMICs). It is largely unknown what ML applications are available for LMICs that can support and advance clinical medicine and public health. We aim to address this gap by conducting a scoping review of health-related ML applications in LMICs. Methods and analysis: This scoping review will follow the methodology proposed by Levac et al. The search strategy is informed by recent systematic reviews of ML health-related applications. We will search Embase, Medline and Global Health (through Ovid), Cochrane and Google Scholar; we will present the date of our searches in the final review. Titles and abstracts will be screened by two reviewers independently; selected reports will be studied by two reviewers independently. Reports will be included if they are primary research where data have been analysed, ML techniques have been used on data from LMICs and they aimed to improve health-related outcomes. We will synthesise the information following evidence mapping recommendations. Ethics and dissemination: The review will provide a comprehensive list of health-related ML applications in LMICs. The results will be disseminated through scientific publications. We also plan to launch a website where ML models can be hosted so that researchers, policymakers and the general public can readily access them. en_US
dc.language.iso eng
dc.publisher BMJ Publishing Group
dc.relation.ispartof urn:issn:2044-6055
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject epidemiology en_US
dc.subject biotechnology & bioinformatics en_US
dc.subject health informatics en_US
dc.subject World Wide Web technology en_US
dc.title Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://doi.org/10.1136/bmjopen-2019-035983
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#3.02.00 es_PE
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#3.02.00


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