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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

BACKGROUND: HIV 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. OBJEC...

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Autores principales: Xu, Xianglong, Yu, Zhen, Ge, Zongyuan, Chow, Eric P F, Bao, Yining, Ong, Jason J, Li, Wei, Wu, Jinrong, Fairley, Christopher K, Zhang, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459839/
https://www.ncbi.nlm.nih.gov/pubmed/36006685
http://dx.doi.org/10.2196/37850
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author Xu, Xianglong
Yu, Zhen
Ge, Zongyuan
Chow, Eric P F
Bao, Yining
Ong, Jason J
Li, Wei
Wu, Jinrong
Fairley, Christopher K
Zhang, Lei
author_facet Xu, Xianglong
Yu, Zhen
Ge, Zongyuan
Chow, Eric P F
Bao, Yining
Ong, Jason J
Li, Wei
Wu, Jinrong
Fairley, Christopher K
Zhang, Lei
author_sort Xu, Xianglong
collection PubMed
description BACKGROUND: HIV 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. OBJECTIVE: The aim of our study was to develop an HIV and STI risk prediction tool using machine learning algorithms. METHODS: We 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. RESULTS: The 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). CONCLUSIONS: Our 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.
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spelling pubmed-94598392022-09-10 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 Xu, Xianglong Yu, Zhen Ge, Zongyuan Chow, Eric P F Bao, Yining Ong, Jason J Li, Wei Wu, Jinrong Fairley, Christopher K Zhang, Lei J Med Internet Res Original Paper BACKGROUND: HIV 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. OBJECTIVE: The aim of our study was to develop an HIV and STI risk prediction tool using machine learning algorithms. METHODS: We 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. RESULTS: The 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). CONCLUSIONS: Our 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. JMIR Publications 2022-08-25 /pmc/articles/PMC9459839/ /pubmed/36006685 http://dx.doi.org/10.2196/37850 Text en ©Xianglong Xu, Zhen Yu, Zongyuan Ge, Eric P F Chow, Yining Bao, Jason J Ong, Wei Li, Jinrong Wu, Christopher K Fairley, Lei Zhang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.08.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Xu, Xianglong
Yu, Zhen
Ge, Zongyuan
Chow, Eric P F
Bao, Yining
Ong, Jason J
Li, Wei
Wu, Jinrong
Fairley, Christopher K
Zhang, Lei
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459839/
https://www.ncbi.nlm.nih.gov/pubmed/36006685
http://dx.doi.org/10.2196/37850
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