查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Kampala,Uganda,by News Rx journalists,research stated,"Antimicrobial resistance (AMR) remains a signi ficant global health threat particularly impacting low- and middle-income countr ies (LMICs). These regions often grapple with limited healthcare resources and a ccess to advanced diagnostic tools." The news correspondents obtained a quote from the research from Makerere Univers ity,"Consequently,there is a pressing need for innovative approaches that can enhance AMR surveillance and management. Machine learning (ML) though underutili zed in these settings,presents a promising avenue. This study leverages ML mode ls 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 indep endent dataset from Africa,specifically from Uganda,Nigeria,and Tanzania,to ascertain their applicability and effectiveness in LMICs. Model performance vari ed across antibiotics. The Support Vector Machine excelled in predicting ciprofl oxacin resistance (87% accuracy,F1 Score: 0.57),Light Gradient B oosting Machine for cefotaxime (92% accuracy,F1 Score: 0.42),and Gradient Boosting for ampicillin (58% accuracy,F1 Score: 0.66). In validation with data from Africa,Logistic Regression showed high accuracy fo r ampicillin (94%,F1 Score: 0.97),while Random Forest and Light G radient Boosting Machine were effective for ciprofloxacin (50% acc uracy,F1 Score: 0.56) and cefotaxime (45% accuracy,F1 Score:0.54 ),respectively. Key mutations associated with AMR were identified for these ant ibiotics. As the threat of AMR continues to rise,the successful application of these models,particularly on genomic datasets from LMICs,signals a promising a venue for improving AMR prediction to support large AMR surveillance programs."
NETL
NSTL