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For: Marcus JL, Sewell WC, Balzer LB, Krakower DS. Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic. Curr HIV/AIDS Rep 2020;17:171-9. [PMID: 32347446 DOI: 10.1007/s11904-020-00490-6] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Ridgway JP, Friedman EE, Bender A, Schmitt J, Cronin M, Brown RN, Johnson AK, Hirschhorn LR. Evaluation of an Electronic Algorithm for Identifying Cisgender Female Pre-Exposure Prophylaxis Candidates. AIDS Patient Care STDS 2021;35:5-8. [PMID: 33400588 DOI: 10.1089/apc.2020.0231] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
2 Jia KM, Eilerts H, Edun O, Lam K, Howes A, Thomas ML, Eaton JW. Risk scores for predicting HIV incidence among adult heterosexual populations in sub-Saharan Africa: a systematic review and meta-analysis. J Int AIDS Soc 2022;25:e25861. [PMID: 35001515 DOI: 10.1002/jia2.25861] [Reference Citation Analysis]
3 Keizur E, Robinson E, Sha BE, Aziz M, Shankaran S. Effectiveness of an electronic health record model for HIV pre-exposure prophylaxis. Int J STD AIDS 2022;33:499-502. [PMID: 35225082 DOI: 10.1177/09564624221079070] [Reference Citation Analysis]
4 Xu X, Ge Z, Chow EPF, Yu Z, Lee D, Wu J, Ong JJ, Fairley CK, Zhang L. A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months. J Clin Med 2022;11:1818. [PMID: 35407428 DOI: 10.3390/jcm11071818] [Reference Citation Analysis]
5 Patel P, Kerzner M, Reed JB, Sullivan P, El-Sadr WM. Public health implications of adapting HIV pre-exposure prophylaxis programs for virtual service delivery in the context of the COVID-19 pandemic: a systematic review. JMIR Public Health Surveill 2022. [PMID: 35486813 DOI: 10.2196/37479] [Reference Citation Analysis]
6 Haas O, Maier A, Rothgang E. Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios. Front Reprod Health 2021;3:756405. [DOI: 10.3389/frph.2021.756405] [Reference Citation Analysis]
7 Weissman S, Yang X, Zhang J, Chen S, Olatosi B, Li X. Using a machine learning approach to explore predictors of healthcare visits as missed opportunities for HIV diagnosis. AIDS 2021;35:S7-S18. [PMID: 33867485 DOI: 10.1097/QAD.0000000000002735] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
8 Ridgway JP, Lee A, Devlin S, Kerman J, Mayampurath A. Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes. Curr HIV/AIDS Rep 2021;18:229-36. [PMID: 33661445 DOI: 10.1007/s11904-021-00552-3] [Reference Citation Analysis]
9 Balzer LB, Petersen ML. Invited Commentary: Machine Learning in Causal Inference-How Do I Love Thee? Let Me Count the Ways. Am J Epidemiol 2021;190:1483-7. [PMID: 33751059 DOI: 10.1093/aje/kwab048] [Reference Citation Analysis]
10 Xiang Y, Du J, Fujimoto K, Li F, Schneider J, Tao C. Application of artificial intelligence and machine learning for HIV prevention interventions. The Lancet HIV 2022;9:e54-62. [DOI: 10.1016/s2352-3018(21)00247-2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
11 Nadarzynski T, Puentes V, Pawlak I, Mendes T, Montgomery I, Bayley J, Ridge D. Barriers and facilitators to engagement with artificial intelligence (AI)-based chatbots for sexual and reproductive health advice: a qualitative analysis. Sex Health 2021;18:385-93. [PMID: 34782055 DOI: 10.1071/SH21123] [Reference Citation Analysis]
12 Oldfield BJ, Edelman EJ. Addressing Unhealthy Alcohol Use and the HIV Pre-exposure Prophylaxis Care Continuum in Primary Care: A Scoping Review. AIDS Behav 2021;25:1777-89. [PMID: 33219492 DOI: 10.1007/s10461-020-03107-6] [Cited by in Crossref: 9] [Cited by in F6Publishing: 3] [Article Influence: 9.0] [Reference Citation Analysis]
13 Young SD, Crowley JS, Vermund SH. Artificial intelligence and sexual health in the USA. Lancet Digit Health 2021;3:e467-8. [PMID: 34325852 DOI: 10.1016/S2589-7500(21)00117-5] [Reference Citation Analysis]
14 Mutai CK, McSharry PE, Ngaruye I, Musabanganji E. Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa. BMC Med Res Methodol 2021;21:159. [PMID: 34332540 DOI: 10.1186/s12874-021-01346-2] [Reference Citation Analysis]
15 Albalawi U, Mustafa M. Current Artificial Intelligence (AI) Techniques, Challenges, and Approaches in Controlling and Fighting COVID-19: A Review. Int J Environ Res Public Health 2022;19:5901. [PMID: 35627437 DOI: 10.3390/ijerph19105901] [Reference Citation Analysis]
16 Garett R, Young SD. The importance of diverse key stakeholders in deciding the role of artificial intelligence for HIV research and policy. Health Policy and Technology 2022;11:100599. [DOI: 10.1016/j.hlpt.2022.100599] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Qiao S, Li X, Olatosi B, Young SD. Utilizing Big Data analytics and electronic health record data in HIV prevention, treatment, and care research: a literature review. AIDS Care 2021;:1-21. [PMID: 34260325 DOI: 10.1080/09540121.2021.1948499] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
18 Bonner R, Stewart J, Upadhyay A, Bruce RD, Taylor JL. A Primary Care Intervention to Increase HIV Pre-Exposure Prophylaxis (PrEP) Uptake in Patients with Syphilis. J Int Assoc Provid AIDS Care 2022;21:23259582211073393. [PMID: 35001723 DOI: 10.1177/23259582211073393] [Reference Citation Analysis]
19 Garett R, Young SD. Potential application of conversational agents in HIV testing uptake among high-risk populations. J Public Health (Oxf) 2022:fdac020. [PMID: 35211740 DOI: 10.1093/pubmed/fdac020] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]