Machine learning for personalized risk assessment of HIV, syphilis, gonorrhoea and chlamydia: A systematic review and meta-analysis

Background: Machine learning (ML) shows promise for sexually transmitted infection (STI) risk prediction, but systematic evidence of its effectiveness remains fragmented. Methods: We systematically searched six electronic databases, three preprint archives and conference proceedings (January 2010-Ap...

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Bibliographic Details

Published in:	International Journal of Infectious Diseases
Main Authors:	Phyu M. Latt, Nyi N. Soe, Christopher K. Fairley, Eric P.  F. Chow, Cheryl C. Johnson, Purvi Shah, Ismail Maatouk, Lei Zhang, Jason J. Ong
Format:	Article
Language:	English
Published:	Elsevier 2025-08-01
Subjects:	Machine learning HIV Sexually transmitted infections Risk assessment Systematic review Meta-analysis
Online Access:	http://www.sciencedirect.com/science/article/pii/S1201971225001456