Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146228
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dc.contributor.authorCarrillo-Larco, Rodrigo M.en_US
dc.contributor.authorTudor Car, Lorainneen_US
dc.contributor.authorPearson-Stuttard, Jonathanen_US
dc.contributor.authorPanch, Trishanen_US
dc.contributor.authorMiranda, J. Jaimeen_US
dc.contributor.authorAtun, Rifaten_US
dc.date.accessioned2021-02-02T09:14:54Z-
dc.date.available2021-02-02T09:14:54Z-
dc.date.issued2020-
dc.identifier.citationCarrillo-Larco, R. M., Tudor Car, L., Pearson-Stuttard, J., Panch, T., Miranda, J. J., & Atun, R. (2020). Machine learning health-related applications in low-income and middle-income countries : a scoping review protocol. BMJ Open, 10(5), e035983-. doi:10.1136/bmjopen-2019-035983en_US
dc.identifier.issn2044-6055en_US
dc.identifier.other0000-0002-2090-1856-
dc.identifier.other0000-0002-4738-5468-
dc.identifier.urihttps://hdl.handle.net/10356/146228-
dc.description.abstractIntroduction: 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.isoenen_US
dc.relation.ispartofBMJ Openen_US
dc.rights© 2020 The Author(s) (or their employer(s)). This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.en_US
dc.subjectScience::Medicineen_US
dc.titleMachine learning health-related applications in low-income and middle-income countries : a scoping review protocolen_US
dc.typeJournal Articleen
dc.contributor.schoolLee Kong Chian School of Medicine (LKCMedicine)en_US
dc.identifier.doi10.1136/bmjopen-2019-035983-
dc.description.versionPublished versionen_US
dc.identifier.pmid32393612-
dc.identifier.scopus2-s2.0-85084541360-
dc.identifier.issue5en_US
dc.identifier.volume10en_US
dc.subject.keywordsBiotechnology & Bioinformaticsen_US
dc.subject.keywordsEpidemiologyen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
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