Generalizability of machine learning in predicting antimicrobial resistance in E. coli: a multi-country case study in Africa.

ABSTRACT:

Background

Antimicrobial resistance (AMR) remains a significant global health threat particularly impacting low- and middle-income countries (LMICs). These regions often grapple with limited healthcare resources and access to advanced diagnostic tools. Consequently, there is a pressing need for innovative approaches that can enhance AMR surveillance and management. Machine learning (ML) though underutilized in these settings, presents a promising avenue. This study leverages ML models trained on whole-genome sequencing data from England, where such data is more readily available, to predict AMR in E. coli, targeting key antibiotics such as ciprofloxacin, ampicillin, and cefotaxime. A crucial part of our work involved the validation of these models using an independent dataset from

SUBMITTER: Nsubuga M 

PROVIDER: S-EPMC10946178 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature