Babafemi Dare
Biographic Data
| ID | 9064647 |
|---|---|
| NAME | Babafemi Dare |
| GIVEN NAMES | Babafemi |
| FAMILY NAME | Dare |
| SIGNATURE | DARE B |
| AFFILIATIONS | Johns Hopkins University |
| ORCID | 0009-0004-3774-3286 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Use of machine learning in predicting continuity of HIV treatment in selected Nigerian States
Nigeria, with the second-largest HIV epidemic globally, faces challenges in achieving its HIV epidemic control goals by 2030, with interruptions in treatment (IIT) a significant challenge. Machine learning (ML) models can help HIV programs implement targeted interventions to improve the quality of care, develop effective early interventions, and provide insights into optimal resource allocation and program sustainability. This paper aims to ident…
Factors associated with viral load re-suppression after enhanced adherence counseling among people living with HIV with an initial high viral load result in selected Nigerian states
The WHO recommends monitoring viral load (VL) to gauge ART efficacy among People Living with HIV (PLHIV). Low suppression rates persist in low- and middle-income countries due to poor adherence. Enhanced Adherence Counseling (EAC) aims to improve adherence and treatment outcomes. This study, part of the Reaching Impact Saturation and Epidemic Control (RISE) project in Nigeria, analyzes factors affecting viral re-suppression post-EAC. It aims to i…
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Factors associated with viral load re-suppression after enhanced adherence counseling among people living with HIV with an initial high viral load result in selected Nigerian states
The WHO recommends monitoring viral load (VL) to gauge ART efficacy among People Living with HIV (PLHIV). Low suppression rates persist in low- and middle-income countries due to poor adherence. Enhanced Adherence Counseling (EAC) aims to improve adherence and treatment outcomes. This study, part of the Reaching Impact Saturation and Epidemic Control (RISE) project in Nigeria, analyzes factors affecting viral re-suppression post-EAC. It aims to i…
Use of machine learning in predicting continuity of HIV treatment in selected Nigerian States
Nigeria, with the second-largest HIV epidemic globally, faces challenges in achieving its HIV epidemic control goals by 2030, with interruptions in treatment (IIT) a significant challenge. Machine learning (ML) models can help HIV programs implement targeted interventions to improve the quality of care, develop effective early interventions, and provide insights into optimal resource allocation and program sustainability. This paper aims to ident…
Family medicine (2 works) · HIV, Drug Use, Sexual Risk (2 works) · HIV/AIDS Research and Interventions (2 works) · Internal Medicine (2 works) · Medicine (2 works) · AdaBoost (1 work) · Artificial Intelligence (1 work) · Cohort (1 work) · Computer Science (1 work) · Confidence interval (1 work)