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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Detalles Bibliográficos
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:
Article
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

Internet

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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