Prediction of First and Multiple Antiretroviral Therapy Interruptions in People Living With HIV: Comparative Survival Analysis Using Cox and Explainable Machine Learning Models
Our results highlight the superior predictive performance of ML models and their ability to provide patient-specific risk predictions and insights that can support targeted interventions to reduce treatment interruptions in ART care.
Authors
- Donald Salami (ORCID: https://orcid.org/0000-0002-4238-5880)
- Stephen Ohakanu (ORCID: https://orcid.org/0000-0002-6487-0166)
- Anthony Ngugi (ORCID: https://orcid.org/0000-0001-5415-8717)
- Emily Koech (ORCID: https://orcid.org/0000-0001-9481-9540)
- Kristen A. Stafford (ORCID: https://orcid.org/0000-0001-5620-6436)
- Janet M. Turan (ORCID: https://orcid.org/0000-0002-7802-6206)
- Lilly M. Nyagah (ORCID: https://orcid.org/0000-0002-3852-6695)
- Manhattan Charurat
Institutions
- University of Maryland, Baltimore (US)
- Alabama Department of Public Health (US)
- Aga Khan University Nairobi (KE)
- National Aids Control Council (KE)
- Ministry of Health (KE)
- Institute of Human Virology (NG)
- International Centre for Reproductive Health Kenya (KE)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-12-30
- DOI
- https://doi.org/10.2196/78964
- Primary Topic
- HIV/AIDS Research and Interventions
- Type
- article
- Field-Weighted Citation Impact
- 0.00