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.

本研究基于 Drouin 等人 (2016) 的相关工作，运用集合覆盖机对抗菌素抗性模型进行高度解释性学习，以及大规模的应用到整个 PATRIC 数据库中，并提出了新的 Kover AMR 平台，用于可视化和解释生成的模型，以期准确预测抗生素表现，根据不同的临床情况，为每个人的治疗计划提供定制服务，从而提高临床结果。