In Antibiotics (Basel, Switzerland)
Antimicrobial resistance (AMR) has become one of the serious global health problems, threatening the effective treatment of a growing number of infections. Machine learning and deep learning show great potential in rapid and accurate AMR predictions. However, a large number of samples for the training of these models is essential. In particular, for novel antibiotics, limited training samples and data imbalance hinder the models' generalization performance and overall accuracy. We propose a deep transfer learning model that can improve model performance for AMR prediction on small, imbalanced datasets. As our approach relies on transfer learning and secondary mutations, it is also applicable to novel antibiotics and emerging resistances in the future and enables quick diagnostics and personalized treatments.
Ren Yunxiao, Chakraborty Trinad, Doijad Swapnil, Falgenhauer Linda, Falgenhauer Jane, Goesmann Alexander, Schwengers Oliver, Heider Dominik
2022-Nov-12
antimicrobial resistance, small data with imbalanced label, transfer learning
