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A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months

Background: More 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 subsequen...

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Autores principales: Xu, Xianglong, Ge, Zongyuan, Chow, Eric P. F., Yu, Zhen, Lee, David, Wu, Jinrong, Ong, Jason J., Fairley, Christopher K., Zhang, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8999359/
https://www.ncbi.nlm.nih.gov/pubmed/35407428
http://dx.doi.org/10.3390/jcm11071818
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author Xu, Xianglong
Ge, Zongyuan
Chow, Eric P. F.
Yu, Zhen
Lee, David
Wu, Jinrong
Ong, Jason J.
Fairley, Christopher K.
Zhang, Lei
author_facet Xu, Xianglong
Ge, Zongyuan
Chow, Eric P. F.
Yu, Zhen
Lee, David
Wu, Jinrong
Ong, Jason J.
Fairley, Christopher K.
Zhang, Lei
author_sort Xu, Xianglong
collection PubMed
description Background: More 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. Methods: Our 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. Results: Our 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. Conclusions: Using 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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spelling pubmed-89993592022-04-12 A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months Xu, Xianglong Ge, Zongyuan Chow, Eric P. F. Yu, Zhen Lee, David Wu, Jinrong Ong, Jason J. Fairley, Christopher K. Zhang, Lei J Clin Med Article Background: More 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. Methods: Our 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. Results: Our 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. Conclusions: Using 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. MDPI 2022-03-25 /pmc/articles/PMC8999359/ /pubmed/35407428 http://dx.doi.org/10.3390/jcm11071818 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Xianglong
Ge, Zongyuan
Chow, Eric P. F.
Yu, Zhen
Lee, David
Wu, Jinrong
Ong, Jason J.
Fairley, Christopher K.
Zhang, Lei
A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months
title A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months
title_full A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months
title_fullStr A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months
title_full_unstemmed A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months
title_short A Machine-Learning-Based Risk-Prediction Tool for HIV and Sexually Transmitted Infections Acquisition over the Next 12 Months
title_sort machine-learning-based risk-prediction tool for hiv and sexually transmitted infections acquisition over the next 12 months
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8999359/
https://www.ncbi.nlm.nih.gov/pubmed/35407428
http://dx.doi.org/10.3390/jcm11071818
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