Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data

Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data

The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitate initial use of empiric (frequently broad-spectrum) antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to inc...

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Bibliographic Details
Main Authors: Mitchell Pesesky, Tahir Hussain, Meghan Wallace, Sanket Patel, Saadia Andleeb, Carey-Ann Burnham, Gautam Dantas
Format: Article
Language:English
Published: Frontiers Media S.A. 2016-11-01
Series:Frontiers in Microbiology
Subjects:
beta-Lactam Resistance
beta-Lactams
Chloramphenicol Resistance
Enterobacteriaceae
Enterobacteriaceae Infections
Tetracycline Resistance
Online Access:http://journal.frontiersin.org/Journal/10.3389/fmicb.2016.01887/full

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