Last Posted: Apr 11, 2024
- The role of community engagement in promoting research participants' understanding of pharmacogenomic research results: Perspectives of stakeholders involved in HIV/AIDS research and treatment.
Sylvia Nabukenya et al. PLoS One 2024 19(4) e0299081 - Machine learning prediction of adolescent HIV testing services in Ethiopia.
Melsew Setegn Alie et al. Front Public Health 2024 121341279 - HIV-1 drug resistance and genetic transmission network among newly diagnosed people living with HIV/AIDS in Ningbo, China between 2018 and 2021.
Hang Hong et al. Virol J 2023 10 (1) 233 - Predicting the HIV/AIDS Knowledge among the Adolescent and Young Adult Population in Peru: Application of Quasi-Binomial Logistic Regression and Machine Learning Algorithms.
Alejandro Aybar-Flores et al. Int J Environ Res Public Health 2023 20(7) - 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 Public Hospitals.
Binyam Tariku Seboka et al. International journal of general medicine 2023 16435-451 - A deliberative public engagement study on heritable human genome editing among South Africans: Study results.
Thaldar Donrich et al. PloS one 2022 17(11) e0275372 - Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios.
Haas Oliver et al. Frontiers in reproductive health 2022 3756405 - Predicting Adolescent Intervention Non-responsiveness for Precision HIV Prevention Using Machine Learning.
Wang Bo et al. AIDS and behavior 2022 - Prevalence of drug resistance and genetic transmission networks among HIV/AIDS patients with antiretroviral therapy failure in Guangxi, China.
Yu Dee et al. AIDS research and human retroviruses 2022 8 - Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China.
Shi Minjuan et al. PLoS neglected tropical diseases 2022 16(5) e0010388 - Predictive analytics using machine learning to identify ART clients at health system level at greatest risk of treatment interruption in Mozambique and Nigeria.
Stockman Jeni et al. Journal of acquired immune deficiency syndromes (1999) 2022 - Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol.
Zhang Jiajia et al. BMC infectious diseases 2022 22(1) 122 - Changing Proportions of HIV-1 Subtypes and Transmitted Drug Resistance Among Newly Diagnosed HIV/AIDS Individuals - China, 2015 and 2018.
Hao Jingjing et al. China CDC weekly 2022 1 (53) 1133-1138 - Prevalence and Molecular Epidemiology of Transmitted Drug Resistance and Genetic Transmission Networks Among Newly Diagnosed People Living With HIV/AIDS in a Minority Area, China.
Yuan Dan et al. Frontiers in public health 2021 10 731280 - Emergence and evolution of big data science in HIV research: Bibliometric analysis of federally sponsored studies 2000-2019.
Liang Chen et al. International journal of medical informatics 2021 154104558 - SARS-CoV-2 Neutralization Resistance Mutations in Patient with HIV/AIDS, California, USA.
Hoffman Seth A et al. Emerging infectious diseases 2021 7 (10) - Diverse experts' perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study.
Nichol Ariadne A et al. BMJ open 2021 11(7) e052287 - Health information technology interventions and engagement in HIV care and achievement of viral suppression in publicly funded settings in the US: A cost-effectiveness analysis.
Shade Starley B et al. PLoS medicine 2021 18(4) e1003389 - Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes.
Ridgway Jessica P et al. Current HIV/AIDS reports 2021 - Use of social media big data as a novel HIV surveillance tool in South Africa.
van Heerden Alastair et al. PloS one 2020 15(10) e0239304
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Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.