Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics.
Antibiotics (Basel)
; 11(11)2022 Nov 12.
Article
en En
| MEDLINE
| ID: mdl-36421255
ABSTRACT
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
2_ODS3
Problema de salud:
2_cobertura_universal
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Antibiotics (Basel)
Año:
2022
Tipo del documento:
Article
País de afiliación:
Alemania