Lucien De Voux
Biographic Data
| ID | 9070677 |
|---|---|
| NAME | Lucien De Voux |
| GIVEN NAMES | Lucien |
| FAMILY NAME | De Voux |
| SIGNATURE | DE VOUX L |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| 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…
Historical visit attendance as predictor of treatment interruption in South African HIV patients
Retention of antiretroviral (ART) patients is a priority for achieving HIV epidemic control in South Africa. While machine-learning methods are being increasingly utilised to identify high risk populations for suboptimal HIV service utilisation, they are limited in terms of explaining relationships between predictors. To further understand these relationships, we implemented machine learning methods optimised for predictive power and traditional …
No prominent works on this page.
Historical visit attendance as predictor of treatment interruption in South African HIV patients
Retention of antiretroviral (ART) patients is a priority for achieving HIV epidemic control in South Africa. While machine-learning methods are being increasingly utilised to identify high risk populations for suboptimal HIV service utilisation, they are limited in terms of explaining relationships between predictors. To further understand these relationships, we implemented machine learning methods optimised for predictive power and traditional …
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…
Artificial Intelligence (2 works) · Computer Science (2 works) · HIV, Drug Use, Sexual Risk (2 works) · HIV/AIDS Research and Interventions (2 works) · Machine learning (2 works) · Medicine (2 works) · AdaBoost (1 work) · Attendance (1 work) · Categorical variable (1 work) · Cohort (1 work)