Predicting suicidality in people living with HIV in Uganda: a machine learning approach
A cost-sensitive AdaBoost model developed using the sociodemographic, psychosocial, and clinical data of PLWH in Uganda can predict suicidality risk, albeit with modest PPV. Incorporating suicidality PRS improved the overall predictive performance of the model. However, larger studies involving more diverse participants are needed to evaluate the potential of PRS in enhancing risk stratification and the clinical utility of the prediction model.
Authors
- Daudi Jjingo (ORCID: https://orcid.org/0000-0003-1685-9234)
- Allan Kalungi (ORCID: https://orcid.org/0000-0002-2890-0145)
- Segun Fatumo (ORCID: https://orcid.org/0000-0003-4525-3362)
- Eugene Kinyanda
- Lillian Linda
- Anthony B. Mutema
Institutions
- AIDS Healthcare Foundation (US)
- Queen Mary University of London (GB)
- London School of Hygiene & Tropical Medicine (GB)
- Makerere University (UG)
- Uganda Virus Research Institute (UG)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2025-08-15
- DOI
- https://doi.org/10.3389/fpsyt.2025.1584335
- Primary Topic
- Suicide and Self-Harm Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European and Developing Countries Clinical Trials Partnership