Evidence map›Paper›PMID 36006685›Full record

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.

Xianglong Xu, Zhen Yu, Zongyuan Ge, Eric P F Chow, Yining Bao, Jason J Ong, Wei Li, Jinrong Wu, Christopher K Fairley, Lei Zhang

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 4 pooled it
9.2field-weighted citation impact, top 1% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 4 syntheses or guidelines pooled it, 59 citations in OpenAlex.

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  3. 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 · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 6 institutions in 2 countries.

Xianglong XuMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.ORCID 0000-0003-1873-4734
Zhen YuCentral Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.ORCID 0000-0001-5210-2672
Zongyuan GeMonash e-Research Centre, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Centre, Monash University, Melbourne, Australia.ORCID 0000-0002-5880-8673
Eric P F ChowMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.ORCID 0000-0003-1766-0657
Yining BaoChina Australia Joint Research Center for Infectious Diseases, Xi'an Jiaotong University Health Science Centre, Xi'an, China.ORCID 0000-0002-3696-5782
Jason J OngMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.ORCID 0000-0001-5784-7403
Wei LiSchool of Public Health, Southeast University, Nanjing, China.ORCID 0000-0003-0769-5805
Jinrong WuResearch Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia.ORCID 0000-0001-8929-0611
Christopher K FairleyMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.ORCID 0000-0001-9081-1664
Lei ZhangMelbourne Sexual Health Centre, Alfred Health, Melbourne, Australia.ORCID 0000-0003-2343-084X
Melbourne Sexual Health Centre · AUMonash University · AULa Trobe University · AUSoutheast University · CNThe University of Melbourne · AUXi'an Jiaotong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Chlamydia InfectionsGonorrheaHIV InfectionsSexually Transmitted DiseasesSyphilisAlgorithmsHomosexuality, MaleHumansInternetMachine LearningMalealgorithmchlamydiadevelopmentgonorrheaHIVmachine learningmodelpredictionpredictiveriskrisk assessmentsexual healthsexually transmittedsexually transmitted infectionssexual transmissionsyphilisvalidationweb-based

Identifiers

PMID36006685
PMCPMC9459839
OpenAlexW4293105715

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.