How Latest Computer Science Research Copes with COVID-19?

Bayoudhi, Leila; Sassi, Najla; Jaziri, Wassim

doi:10.1007/978-3-030-96308-8_112

Cited by 1 publication

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These exhibit a shift toward topics related to the pandemic, such as ML for virology and ML for epidemiology. The increase of African research productivity for healthcare since 2020 is in line with the global efforts to solve urgent matters related to the outbreak, particularly those related to timely data curation and management [39,40]. This also reaffirms previous observations that disease outbreaks can lead to the growth of research in underdeveloped fields [41] for affected nations [42].…”

Section: Publishing Patternssupporting

confidence: 77%

Machine Learning for Healthcare: A Bibliometric Study of Contributions from Africa

Turki¹,

Pouris²,

Ifeanyichukwu³

et al. 2023

Preprint

View full text Add to dashboard Cite

Machine learning has seen enormous growth in the last decade, with healthcare being a prime application for advanced diagnostics and improved patient care. The application of machine learning for healthcare is particularly pertinent in Africa, where many countries are resource-scarce. However, it is unclear how much research on this topic is arising from African institutes themselves, which is a crucial aspect for applications of machine learning to unique contexts and challenges on the continent. Here, we conduct a bibliometric study of African contributions to research publications related to machine learning for healthcare, as indexed in Scopus, between 1993 and 2022. We identified 3,772 research outputs, with most of these published since 2020. North African countries currently lead the way with 64.5% of publications for the reported period, yet Sub-Saharan Africa is rapidly increasing its output. We found that international support in the form of funding and collaborations is correlated with research output generally for the continent, with local support garnering less attention. Understanding African research contributions to machine learning for healthcare is a crucial first step in surveying the broader academic landscape, forming stronger research communities, and providing advanced and contextually aware biomedical access to Africa.

show abstract