ArticleScientific reports2022
Using machine learning approaches to predict timely clinic attendance and the uptake of HIV/STI testing post clinic reminder messages.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for HIV care: a global systematic review of current studies and emerging trends.Journal of the International AIDS Society · 2025Pooled it
- A machine learning-driven early warning system for cryptocaryoniasis in marine aquaculture.Parasites & vectors · 2025Article
- 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.Journal of medical Internet research · 2025Article
- The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population-Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis.JMIR research protocols · 2025Article
- Enhancing HIV/STI decision-making: challenges and opportunities in leveraging predictive models for individuals, healthcare providers, and policymakers.Journal of translational medicine · 2024Review
- Predictors of Medical and Dental Clinic Closure by Machine Learning Methods: Cross-Sectional Study Using Empirical Data.Journal of medical Internet research · 2024Article
- STI/HIV risk prediction model development-A novel use of public data to forecast STIs/HIV risk for men who have sex with men.Frontiers in public health · 2024Article
- Machine learning prediction of adolescent HIV testing services in Ethiopia.Frontiers in public health · 2024Article
- Determinants and prediction ofFrontiers in public health · 2022Article
- Article
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7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Timely and regular testing for HIV and sexually transmitted infections (STI) is important for controlling HIV and STI (HIV/STI) among men who have sex with men (MSM). We established multiple machine learning models (e.g., logistic regression, lasso regression, ridge regression, elastic net regression, support vector machine, k-nearest neighbour, naïve bayes, random forest, gradient boosting machine, XGBoost, and multi-layer perceptron) to predict timely (i.e., within 30 days) clinic attendance and HIV/STI testing uptake after receiving a reminder message via short message service (SMS) or email). Our study used 3044 clinic consultations among MSM within 12 months after receiving an email or SMS reminder at the Melbourne Sexual Health Centre between April 11, 2019, and April 30, 2020. About 29.5% [899/3044] were timely clinic attendance post reminder messages, and 84.6% [761/899] had HIV/STI testing. The XGBoost model performed best in predicting timely clinic attendance [mean [SD] AUC 62.8% (3.2%); F1 score 70.8% (1.2%)]. The elastic net regression model performed best in predicting HIV/STI testing within 30 days [AUC 82.7% (6.3%); F1 score 85.3% (1.8%)]. The machine learning approach is helpful in predicting timely clinic attendance and HIV/STI re-testing. Our predictive models could be incorporated into clinic websites to inform sexual health care or follow-up service.
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