Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol
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 cou...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2020-05-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/10/5/e035983.full |
| _version_ | 1818507837819584512 |
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| author | J Jaime Miranda Jonathan Pearson-Stuttard Lorainne Tudor Car Rodrigo M Carrillo-Larco Trishan Panch |
| author_facet | J Jaime Miranda Jonathan Pearson-Stuttard Lorainne Tudor Car Rodrigo M Carrillo-Larco Trishan Panch |
| author_sort | J Jaime Miranda |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T22:23:35Z |
| format | Article |
| id | doaj.art-e313d8a66e7049e7ad844d1e460ca4a0 |
| institution | Directory Open Access Journal |
| issn | 2044-6055 |
| language | English |
| last_indexed | 2024-12-10T22:23:35Z |
| publishDate | 2020-05-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj.art-e313d8a66e7049e7ad844d1e460ca4a02022-12-22T01:31:15ZengBMJ Publishing GroupBMJ Open2044-60552020-05-0110510.1136/bmjopen-2019-035983Machine learning health-related applications in low-income and middle-income countries: a scoping review protocolJ Jaime MirandaJonathan Pearson-StuttardLorainne Tudor CarRodrigo M Carrillo-LarcoTrishan PanchIntroduction 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.https://bmjopen.bmj.com/content/10/5/e035983.full |
| spellingShingle | J Jaime Miranda Jonathan Pearson-Stuttard Lorainne Tudor Car Rodrigo M Carrillo-Larco Trishan Panch Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol BMJ Open |
| title | Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol |
| title_full | Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol |
| title_fullStr | Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol |
| title_full_unstemmed | Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol |
| title_short | Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol |
| title_sort | machine learning health related applications in low income and middle income countries a scoping review protocol |
| url | https://bmjopen.bmj.com/content/10/5/e035983.full |
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