ArticleJournal of clinical medicine2022
A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence-based risk assessment tools for sexual, reproductive and mental health: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Computational methods applied to syphilis: where are we, and where are we going?Frontiers in public health · 2023Pooled it
- Findings from a transformer-based prediction model: social norms and confidentiality are associated with STIs risk in British men and women.BMC public health · 2026Article
- Using deep learning systems for diagnosing common skin lesions in sexual health.Communications medicine · 2025Article
- Application of Machine Learning and Emerging Health Technologies in the Uptake of HIV Testing: Bibliometric Analysis of Studies Published From 2000 to 2024.Interactive journal of medical research · 2025Article
- Role of Artificial Intelligence and Personalized Medicine in Enhancing HIV Management and Treatment Outcomes.Life (Basel, Switzerland) · 2025Review
- What Do People Want from an AI-Assisted Screening App for Sexually Transmitted Infection-Related Anogenital Lesions: A Discrete Choice Experiment.The patient · 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
- Accuracy of symptom checker for the diagnosis of sexually transmitted infections using machine learning and Bayesian network algorithms.BMC infectious diseases · 2024Article
- Enhancing HIV/STI decision-making: challenges and opportunities in leveraging predictive models for individuals, healthcare providers, and policymakers.Journal of translational medicine · 2024Review
- Using AI to Differentiate Mpox From Common Skin Lesions in a Sexual Health Clinic: Algorithm Development and Validation Study.Journal of medical Internet research · 2024Article
- Evaluation of artificial intelligence-powered screening for sexually transmitted infections-related skin lesions using clinical images and metadata.BMC medicine · 2024Article
- The Development and Performance of a Machine-Learning Based Mobile Platform for Visually Determining the Etiology of 5 Penile Diseases.Mayo Clinic proceedings. Digital health · 2024Article
- Lightweight federated learning for STIs/HIV prediction.Scientific reports · 2024Article
- Identifying Individuals at High Risk for HIV and Sexually Transmitted Infections With an Artificial Intelligence-Based Risk Assessment Tool.Open forum infectious diseases · 2024Article
- Are mHealth Interventions Effective in Improving the Uptake of Sexual and Reproductive Health Services among Adolescents? A Scoping Review.International journal of environmental research and public health · 2024Article
- Building gender-specific sexually transmitted infection risk prediction models using CatBoost algorithm and NHANES data.BMC medical informatics and decision making · 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
- Does Maternal HIV Infection Affect Neonatal Outcomes?Current HIV research · 2024Article
- Assessing disparity in the distribution of HIV and sexually transmitted infections in Australia: a retrospective cross-sectional study using Gini coefficients.BMJ public health · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundMore than one million people acquire sexually transmitted infections (STIs) every day globally. It is possible that predicting an individual's future risk of HIV/STIs could contribute to behaviour change or improve testing. We developed a series of machine learning models and a subsequent risk-prediction tool for predicting the risk of HIV/STIs over the next 12 months.
methodsOur data included individuals who were re-tested at the clinic for HIV (65,043 consultations), syphilis (56,889 consultations), gonorrhoea (60,598 consultations), and chlamydia (63,529 consultations) after initial consultations at the largest public sexual health centre in Melbourne from 2 March 2015 to 31 December 2019. We used the receiver operating characteristic (AUC) curve to evaluate the model's performance. The HIV/STI risk-prediction tool was delivered via a web application.
resultsOur risk-prediction tool had an acceptable performance on the testing datasets for predicting HIV (AUC = 0.72), syphilis (AUC = 0.75), gonorrhoea (AUC = 0.73), and chlamydia (AUC = 0.67) acquisition.
conclusionsUsing machine learning techniques, our risk-prediction tool has acceptable reliability in predicting HIV/STI acquisition over the next 12 months. This tool may be used on clinic websites or digital health platforms to form part of an intervention tool to increase testing or reduce future HIV/STI risk.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.