This paper is in the following e-collection/theme issue:

Public (e)Health, Digital Epidemiology and Public Health Informatics (1051) HIV/AIDS/STI Prevention and Care (1062) LGBTQ Issues (556) Machine Learning (2166) Risky Sexual Behavior (177) Health and Risk Communication (83) mHealth for Data Collection and Research (1219) Cross-Sectional Studies in Public Health (451)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59101, first published .
Predicting the Risk of HIV Infection and Sexually Transmitted Diseases Among Men Who Have Sex With Men: Cross-Sectional Study Using Multiple Machine Learning Approaches

Predicting the Risk of HIV Infection and Sexually Transmitted Diseases Among Men Who Have Sex With Men: Cross-Sectional Study Using Multiple Machine Learning Approaches

Predicting the Risk of HIV Infection and Sexually Transmitted Diseases Among Men Who Have Sex With Men: Cross-Sectional Study Using Multiple Machine Learning Approaches

Authors of this article:

Bing Lin1, 2 Author Orcid Image ;   Jiaxiu Liu3 Author Orcid Image ;   Kangjie Li1, 2 Author Orcid Image ;   Xiaoni Zhong1, 2 Author Orcid Image
Article Authors Cited by (7) Tweetations Metrics

Journals

  1. Abdelouahed M, Yateem D, Amzil C, Aribi I, Abdelwahed E, Fredericks S. Integrating artificial intelligence into public health education and healthcare: insights from the COVID-19 and monkeypox crises for future pandemic readiness. Frontiers in Education 2025;10 View
  2. Jaiteh M, Phalane E, Shiferaw Y, Phaswana-Mafuya R. The status of machine learning in HIV testing in South Africa: a qualitative inquiry with stakeholders in Gauteng province. Frontiers in Digital Health 2025;7 View
  3. Yang X, Liu Q, Xue H, Zhang G, Tang H. Adherence to on-demand and daily oral pre-exposure prophylaxis for HIV among men who have sex with men in China: a cross-sectional study. BMC Infectious Diseases 2025;25(1) View
  4. Young S. Artificial intelligence in HIV research, policy, and clinical care. The Lancet HIV 2026;13(2):e138 View
  5. Sun Q, Zhang K, Xu Y, Luo M, Yang Z, Liu Q, Liu S, Liu A. Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study. BMC Infectious Diseases 2025;25(1) View
  6. Li L, Shi C, Liu X, Li W, Luo Y, Zhang H, Wang G, Zhao Y, Huang Y, Yang J, Yang J, Li S, Shen L. A diagnostic model for NTM disease in HIV-positive patients: a machine learning-based analysis with novel inflammatory markers. Frontiers in Immunology 2025;16 View

Conference Proceedings

  1. Ho N, Salim A, Gohtama G, Lucky H, Suhartono D. 2025 7th International Conference on Cybernetics and Intelligent System (ICORIS). Machine Learning Algorithms for HIV/AIDS Prediction using Explainable AI View