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Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol
  1. Rodrigo M Carrillo-Larco1,2,
  2. Lorainne Tudor Car3,4,
  3. Jonathan Pearson-Stuttard5,
  4. Trishan Panch6,
  5. J Jaime Miranda2,7,
  6. Rifat Atun8
  1. 1Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
  2. 2CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
  3. 3Family Medicine and Primary Care, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
  4. 4Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK
  5. 5Department of Epidemiology and Biostatistics and MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
  6. 6Wellframe Inc, Boston, Massachusetts, USA
  7. 7Facultad de Medicina "Alberto Hurtado", Universidad Peruana Cayetano Heredia, Lima, Peru
  8. 8Harvard T.H Chan School of Public Health and Harvard Medical School, Harvard University, Cambridge, Massachusetts, USA
  1. Correspondence to Rodrigo M Carrillo-Larco; r.carrillo-larco{at}


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.

  • epidemiology
  • biotechnology & bioinformatics
  • health informatics
  • World Wide Web technology

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:

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  • Contributors RMC-L conceived the idea and drafted the manuscript. JP-S, TP and RA provided advice to improve the research question and LTC to improve the protocol. JJM and RA edited and provided insights to improve the protocol. All authors approved the submitted version.

  • Funding RMC-L has been supported by a Strategic Award, Wellcome Trust-Imperial College Centre for Global Health Research (100693/Z/12/Z) and Imperial College London Wellcome Trust Institutional Strategic Support Fund (Global Health Clinical Research Training Fellowship) (294834/Z/16/Z ISSF ICL). RMC-L is supported by a Wellcome Trust International Training Fellowship (214185/Z/18/Z). The funders had no role in this work and decision to submit for publication.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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