Abstract：
:Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ±1 two-fold dilution factor, is 92%. Individual accuracies are ≥90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Nguyen M,Brettin T,Long SW,Musser JM,Olsen RJ,Olson R,Shukla M,Stevens RL,Xia F,Yoo H,Davis JJdoi
10.1038/s41598-017-18972-wsubject
Has Abstractpub_date
2018-01-11 00:00:00pages
421issue
1issn
2045-2322pii
10.1038/s41598-017-18972-wjournal_volume
8pub_type
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