Antibiotic susceptibility prediction using transformers

J. Inda-Diaz, A. Johnning, A. Sjöberg, A. Lokrantz, M. Jirstrand, M. Hessel, L. Helldal, L. Svensson, E. Kristiansson. Pacific Symposium on Biocomputing (PSB) 2023, January 3-7, Hawaii, USA.



Antibiotic treatment depends on the susceptibility of the bacteria, typically estimated using cultivation-based methods that can be slow and time-consuming. Physicians empirical treatment has a considerable chance of failing or being unnecessarily broad.


Application of Natural Language Processing tools to predict antibiotic resistance from incomplete data. Development of a deep learning model that predicts the susceptibility of bacterial isolates to antibiotics. Uncertainty control to make predictions with pre-specified confidence for all possible outputs.


Susceptibility test data for 413,593 isolates from 30 European countries retrieved from The European Centre for Disease Prevention and Control. The isolates correspond to Escherichia coli obtained from blood samples.

Photo credits: Nic McPhee