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

Rodrigo M Carrillo-Larco^1,2, Lorainne Tudor Car^3,4, Jonathan Pearson-Stuttard⁵, Trishan Panch⁶, J Jaime Miranda^2,7, Rifat Atun⁸

¹Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK r.carrillo-larco@imperial.ac.uk.

²CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru.

³Family Medicine and Primary Care, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.

⁴Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK.

⁵Department of Epidemiology and Biostatistics and MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.

⁶Wellframe Inc, Boston, Massachusetts, USA.

⁷Facultad de Medicina "Alberto Hurtado", Universidad Peruana Cayetano Heredia, Lima, Peru.

⁸Harvard T.H Chan School of Public Health and Harvard Medical School, Harvard University, Cambridge, Massachusetts, USA.

PubMed PMC DOI PDF

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…

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