Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea)
This study demonstrated the capability to predict VL suppression but has some limitations. The results are dependent on the quality of the data and are specific to the Guinea context and thus, there may be limitations with generalizability. Future studies may be to conduct a similar study in a different context and develop the most optimal model into an application that can be tested in a clinical context.
Authors
- Dègninou Yehadji (ORCID: https://orcid.org/0000-0003-3603-2816)
- Petros Isaakidis (ORCID: https://orcid.org/0000-0002-7881-1554)
- Geraldine Gray (ORCID: https://orcid.org/0000-0002-9694-2558)
- Abdourahimi Diallo
- Carlos Arias Vicente
- S. Kamano
- Thierno Saidou Diallo
Institutions
- University of Ioannina (GR)
- Médecins Sans Frontières (ZA)
- Technological University Dublin (IE)
- Ministry of Health and Public Hygiene (GN)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-03-19
- DOI
- https://doi.org/10.3389/frai.2025.1446876
- Citations
- 1
- Primary Topic
- HIV/AIDS Research and Interventions
- Type
- article
- Field-Weighted Citation Impact
- 1.40