ArticleJournal of medical Internet research2022
Web-Based Risk Prediction Tool for an Individual's Risk of HIV and Sexually Transmitted Infections Using Machine Learning Algorithms: Development and External Validation Study.
Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 4 syntheses or guidelines pooled it, 59 citations in OpenAlex.
- Risk prediction models for targeted testing of HIV, hepatitis B and hepatitis C: a systematic review and meta-analysis.BMC infectious diseases · 2025Pooled it
- Use of AI in Identification of Sexually Transmitted Infections and Anogenital Dermatoses: A Systematic Review and Meta-Analysis.JAMA network open · 2025Pooled it
- Machine learning for personalized risk assessment of HIV, syphilis, gonorrhoea and chlamydia: A systematic review and meta-analysis.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2025Pooled it
- Artificial intelligence-based risk assessment tools for sexual, reproductive and mental health: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Artificial Intelligence in the Assessment of Males with Chronic Pelvic Pain Syndrome: An Up-to-Date UPOINTS-Based Narrative Mapping Review.Diagnostics (Basel, Switzerland) · 2026Review
- Awareness, Acceptance, and Uptake of HIV Pre-Exposure Prophylaxis among Men Who Have Sex with Men (MSM) in Northwestern China: A Cross-Sectional Study.AIDS and behavior · 2026Article
- "O for KP": an evidence review on recentering HIV prevention and PrEP delivery for key populations and the underserved.Current opinion in infectious diseases · 2026Review
- Research on the severity of symptoms in children with ASD based on integrated machine learning and structural equation modeling: age-specific predictive features and mediation effect path analysis.Frontiers in pediatrics · 2026Article
- External validation of a web- and artificial intelligence-based HIV/STI risk assessment tool: performance evaluation using data from Sydney sexual health centre.BMC infectious diseases · 2025Article
- Anxiety responses and testing intentions among gay and bisexual men using an AI-powered HIV/STI risk assessment tool: a quasi-experimental study.BMC public health · 2025Article
- Using deep learning systems for diagnosing common skin lesions in sexual health.Communications medicine · 2025Article
- Developing and validating a Bayesian clinical risk prediction model for three sexually transmitted infections in key populations from two Canadian provinces.Sexually transmitted infections · 2025Article
- Radiomics analysis for the early diagnosis of common sexually transmitted infections and skin lesions.PLOS digital health · 2025Article
- Impact of Result Displays in an Anogenital Symptom Checker App on Health-seeking Behaviours: A Cross-sectional, Vignette-based Study.Open forum infectious diseases · 2025Article
- Urban and rural disparities in stroke prediction using machine learning among Chinese older adults.Scientific reports · 2025Article
- Prediction of mortality risk in critically ill patients with systemic lupus erythematosus: a machine learning approach using the MIMIC-IV database.Lupus science & medicine · 2025Observational
- Prediction of subjective well-being level in residents of Dali City: Where modern tourism meets traditional ethnic culture.PloS one · 2025Article
- Identifying High-Risk Populations for Sexually Transmitted Infections in Chinese Men Who Have Sex With Men: A Cluster Analysis.Open forum infectious diseases · 2025Article
- Prediction model of in-hospital mortality risk in intensive care unit patients with cardiac arrest: a multicenter retrospective cohort study based on an ensemble model.Frontiers in cardiovascular medicine · 2025Article
- Accuracy of symptom checker for the diagnosis of sexually transmitted infections using machine learning and Bayesian network algorithms.BMC infectious diseases · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHIV and sexually transmitted infections (STIs) are major global public health concerns. Over 1 million curable STIs occur every day among people aged 15 years to 49 years worldwide. Insufficient testing or screening substantially impedes the elimination of HIV and STI transmission.
objectiveThe aim of our study was to develop an HIV and STI risk prediction tool using machine learning algorithms.
methodsWe used clinic consultations that tested for HIV and STIs at the Melbourne Sexual Health Centre between March 2, 2015, and December 31, 2018, as the development data set (training and testing data set). We also used 2 external validation data sets, including data from 2019 as external "validation data 1" and data from January 2020 and January 2021 as external "validation data 2." We developed 34 machine learning models to assess the risk of acquiring HIV, syphilis, gonorrhea, and chlamydia. We created an online tool to generate an individual's risk of HIV or an STI.
resultsThe important predictors for HIV and STI risk were gender, age, men who reported having sex with men, number of casual sexual partners, and condom use. Our machine learning-based risk prediction tool, named MySTIRisk, performed at an acceptable or excellent level on testing data sets (area under the curve [AUC] for HIV=0.78; AUC for syphilis=0.84; AUC for gonorrhea=0.78; AUC for chlamydia=0.70) and had stable performance on both external validation data from 2019 (AUC for HIV=0.79; AUC for syphilis=0.85; AUC for gonorrhea=0.81; AUC for chlamydia=0.69) and data from 2020-2021 (AUC for HIV=0.71; AUC for syphilis=0.84; AUC for gonorrhea=0.79; AUC for chlamydia=0.69).
conclusionsOur web-based risk prediction tool could accurately predict the risk of HIV and STIs for clinic attendees using simple self-reported questions. MySTIRisk could serve as an HIV and STI screening tool on clinic websites or digital health platforms to encourage individuals at risk of HIV or an STI to be tested or start HIV pre-exposure prophylaxis. The public can use this tool to assess their risk and then decide if they would attend a clinic for testing. Clinicians or public health workers can use this tool to identify high-risk individuals for further interventions.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.