Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2023 Sep 29;14(1):6084.
doi: 10.1038/s41467-023-41809-2.

The promise of data science for health research in Africa

Affiliations
Review

The promise of data science for health research in Africa

Clement A Adebamowo et al. Nat Commun. .

Abstract

Data science health research promises tremendous benefits for African populations, but its implementation is fraught with substantial ethical governance risks that could thwart the delivery of these anticipated benefits. We discuss emerging efforts to build ethical governance frameworks for data science health research in Africa and the opportunities to advance these through investments by African governments and institutions, international funding organizations and collaborations for research and capacity development.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Similar articles

Cited by

References

    1. Keshavamurthy R, Dixon S, Pazdernik KT, Charles LE. Predicting infectious disease for biopreparedness and response: a systematic review of machine learning and deep learning approaches. One Health. 2022;15:100439. doi: 10.1016/j.onehlt.2022.100439. - DOI - PMC - PubMed
    1. Tanser FC, le Sueur D. The application of geographical information systems to important public health problems in Africa. Int. J. Health Geogr. 2002;1:4. doi: 10.1186/1476-072X-1-4. - DOI - PMC - PubMed
    1. Stewart K, et al. Modeling spatial access to cervical cancer screening services in Ondo State, Nigeria. Int J. Health Geogr. 2020;19:28. doi: 10.1186/s12942-020-00222-4. - DOI - PMC - PubMed
    1. Georgakopoulos, S. V., Gallos, P. & Plagianakos, V. P. Using Big Data Analytics to Detect Fraud in Healthcare Provision in 2020 IEEE 5th Middle East and Africa Conference on Biomedical Engineering (MECBME 2020). (IEEE, New Jersy, 2020).
    1. Gebremeskel GB, Yi C, He Z, Haile D. Combined data mining techniques based patient data outlier detection for healthcare safety. Int. J. Intell. Comput. Cybern. 2016;9:42–68. doi: 10.1108/IJICC-07-2015-0024. - DOI

Publication types