Predicting Treatment Interruption Among People Living With HIV in Nigeria: Machine Learning Approach.
Matthew-David OgbechieChrista Fischer WalkerMu-Tien LeeAmina Abba GanaAbimbola OduolaAugustine IdemudiaMatthew EdorEmily Lark HarrisJessica StephensXiaoming GaoPai-Lien ChenNavindra Etwaroo PersaudPublished in: JMIR AI (2023)
High-performing ML models to identify patients with HIV at risk of IIT can be developed using routinely collected service delivery data and integrated into routine health management information systems. Machine learning can improve the targeting of interventions through differentiated models of care before patients interrupt treatment, resulting in increased cost-effectiveness and improved patient outcomes.
Keyphrases
- machine learning
- healthcare
- end stage renal disease
- mental health
- big data
- chronic kidney disease
- artificial intelligence
- ejection fraction
- palliative care
- human immunodeficiency virus
- antiretroviral therapy
- hiv positive
- prognostic factors
- cancer therapy
- risk assessment
- hiv aids
- hiv testing
- patient reported outcomes
- pain management