Large scale modeling of antimicrobial resistance with interpretable classifiers
This work addresses the public health concern of antimicrobial resistance by enabling tailored treatment plans for bacterial infections, though it is incremental as it applies an existing method to new data.
The authors tackled the problem of predicting antimicrobial resistance from genome sequences by applying an existing interpretable classification method to a large-scale database, achieving results for 36 new datasets and introducing a web-based tool for model visualization.
Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide. Accurately predicting resistance phenotypes from genome sequences shows great promise in promoting better use of antimicrobial agents, by determining which antibiotics are likely to be effective in specific clinical cases. In healthcare, this would allow for the design of treatment plans tailored for specific individuals, likely resulting in better clinical outcomes for patients with bacterial infections. In this work, we present the recent work of Drouin et al. (2016) on using Set Covering Machines to learn highly interpretable models of antibiotic resistance and complement it by providing a large scale application of their method to the entire PATRIC database. We report prediction results for 36 new datasets and present the Kover AMR platform, a new web-based tool allowing the visualization and interpretation of the generated models.