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

ABSTRACT:

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-April 2024). Studies reporting quantitative performance metrics for supervised ML-based STI risk prediction models were included. We used a bivariate random-effects model to estimate pooled sensitivity, specificity and area under the curve (AUC). The risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool. We conducted sequential analyses of studies with complete and reconstructed confusion matrices. Subgroup analyses and meta-regression explored potential so

SUBMITTER: Latt PM 

PROVIDER: S-EPMC12181084 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature