Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146228
Title: Machine learning health-related applications in low-income and middle-income countries : a scoping review protocol
Authors: Carrillo-Larco, Rodrigo M.
Tudor Car, Lorainne
Pearson-Stuttard, Jonathan
Panch, Trishan
Miranda, J. Jaime
Atun, Rifat
Keywords: Science::Medicine
Issue Date: 2020
Source: Carrillo-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-2019-035983
Journal: BMJ Open
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.
URI: https://hdl.handle.net/10356/146228
ISSN: 2044-6055
DOI: 10.1136/bmjopen-2019-035983
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/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles

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