机器学习方法在预测细菌耐药表型中的应用OA北大核心

With the rapid development of the economy and the increasing demand for medical services in China,the use of antimicrobials in human clinical,pet clinical and husbandry industries has become more frequent,This has led to an increasingly serious problem of antimicrobial resistance(AMR),posing a potential threat to public health security.Rapid and accurate detection of AMR phenotypes can effectively guide clinical diagnosis and treatment of infectious diseases,reducing the risk of AMR caused by empirical and irrational drug use.However,the existing detection technologies are time-consuming and cumbersome,making it difficult to be widely used in clinical practice.Moreover,single-type rapid detection reagents and similar products cannot meet the diverse clinical needs.Therefore,there is an urgent need to develop new technologies to provide effective solutions for rapid identification of AMR phenotypes in clinic.Genomic information of bacteria contains a wealth of features related to AMR phenotypes.Rapid and accurate extraction of relevant information from this data can assist in rapid diagnosis and treatment.Machine learning models have significant advantages in processing complex structured data and have shown great application potential in mining genomic information.With the rapid development of this field,machine learning methods are expected to provide technical support for rapid and accurate prediction of AMR phenotypes in clinical practice,and help doctors improve the accuracy of diagnosis and treatment.This review systematically summarizes the current research status and development trends of machine learning models in the field of predicting AMR phenotypes,compares the characteristics and performance of different machine learning methods,and summarizes the key elements required for predicting and modeling AMR phenotypes,providing references for subsequent relevant research.