Artificial intelligence and machine learning based prediction of viral load and CD4 status of people living with HIV (PLWH) on anti-retroviral treatment in Gedeo Zone …

BT Seboka, DE Yehualashet… - International Journal of …, 2023 - Taylor & Francis
Background Despite the success made in scaling up HIV treatment activities, there remains
a tremendous unmet demand for the monitoring of the disease progression and treatment …

Historical visit attendance as predictor of treatment interruption in South African HIV patients: Extension of a validated machine learning model

RT Esra, J Carstens, J Estill, R Stoch… - PLOS Global Public …, 2023 - journals.plos.org
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 …

Development and validation of a prediction tool to support engagement in HIV care among young people ages 10–24 years in Kenya

K Wilson, K Agot, J Dyer, J Badia, J Kibugi, R Bosire… - Plos one, 2023 - journals.plos.org
Introduction Loss to follow-up (LTFU) among adolescents and young adults living with HIV
(AYALWH) is a barrier to optimal health and HIV services. We developed and validated a …

IAPAC–Lancet HIV Commission on the future of urban HIV responses

JM Zuniga, C Prachniak, N Policek, N Magula… - The Lancet …, 2024 - thelancet.com
Executive summary With urbanisation trends projecting more than twice as many people
globally will be living in urban settings than rural ones by 2050, cities have an increasingly …

Machine learning to predict virological failure among HIV patients on antiretroviral therapy in the University of Gondar Comprehensive and Specialized Hospital, in …

DN Mamo, TM Yilma, M Fekadie, Y Sebastian… - BMC Medical Informatics …, 2023 - Springer
Background Treatment with effective antiretroviral therapy (ART) reduces viral load as well
as HIV-related morbidity and mortality in HIV-positive patients. Despite the expanded …

Achieving the third 95 in sub-Saharan Africa: application of machine learning approaches to predict viral failure

AL Esber, NF Dear, D King, LV Francisco, V Sing'oei… - AIDS, 2023 - journals.lww.com
Objective: Viral failure in people with HIV (PWH) may be influenced by multiple
sociobehavioral, clinical, and context-specific factors, and supervised learning approaches …

Patient Dropout Prediction in Virtual Health: A Multimodal Dynamic Knowledge Graph and Text Mining Approach

S Geng, W Zhang, J Xie, G Liang, B Niu - arXiv preprint arXiv:2306.03833, 2023 - arxiv.org
Virtual health has been acclaimed as a transformative force in healthcare delivery. Yet, its
dropout issue is critical that leads to poor health outcomes, increased health, societal, and …

Rudi Kundini, Pamoja Kundini (RKPK): study protocol for a hybrid type 1 randomized effectiveness-implementation trial using data science and economic incentive …

JL Kadota, LJ Packel, M Mlowe, N Ulenga, N Mwenda… - Trials, 2024 - Springer
Background Economic incentives can improve clinical outcomes among in-care people
living with HIV (PLHIV), but evidence is limited for their effectiveness among out-of-care …

Exploring the Feasibility of an Electronic Tool for Predicting Retention in HIV Care: Provider Perspectives

J Kromash, EE Friedman, SA Devlin, J Schmitt… - International Journal of …, 2024 - mdpi.com
Retention in care for people living with HIV (PLWH) is important for individual and
population health. Preemptive identification of PLWH at high risk of lapsing in care may …

Closing the gap in paediatric HIV infections: how available tools and technology can accelerate progress towards ending AIDS by 2030

W Mutale, ME Herce - The Lancet, 2024 - thelancet.com
Despite declining HIV incidence and AIDS-related deaths globally, progress against HIV
elimination remains fragile in many countries and acutely inadequate among priority …