首页|Data on Machine Learning Reported by Alexander Sturm and Colleagues (Accurate an d rapid antibiotic susceptibility testing using a machine learning-assisted nano motion technology platform)
Data on Machine Learning Reported by Alexander Sturm and Colleagues (Accurate an d rapid antibiotic susceptibility testing using a machine learning-assisted nano motion technology platform)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
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 originating from Muttenz, Swi tzerland, by NewsRx correspondents, research stated, "Antimicrobial resistance ( AMR) is a major public health threat, reducing treatment options for infected pa tients. AMR is promoted by a lack of access to rapid antibiotic susceptibility t ests (ASTs)." Our news editors obtained a quote from the research, "Accelerated ASTs can ident ify effective antibiotics for treatment in a timely and informed manner. We desc ribe a rapid growth-independent phenotypic AST that uses a nanomotion technology platform to measure bacterial vibrations. Machine learning techniques are appli ed to analyze a large dataset encompassing 2762 individual nanomotion recordings from 1180 spiked positive blood culture samples covering 364 Escherichia coli and Klebsiella pneumoniae isolates exposed to cephalosporins and fluoroquinolones. The training performan ces of the different classification models achieve between 90.5 and 100% accuracy. Independent testing of the AST on 223 strains, including in clinical s etting, correctly predict susceptibility and resistance with accuracies between 89.5% and 98.9 %."
MuttenzSwitzerlandEuropeBusinessCyborgsDrugs and TherapiesEmerging TechnologiesMachine LearningTechnolog y
NETL
NSTL
