Statistical methods for automated drug susceptibility testing: Bayesian minimum inhibitory concentration prediction from growth curves

@article{Zhou2009StatisticalMF,
  title={Statistical methods for automated drug susceptibility testing: Bayesian minimum inhibitory concentration prediction from growth curves},
  author={Xi Kathy Zhou and Merlise A. Clyde and James Garrett and Viridiana Lourdes and Michael O’Connell and Giovanni Parmigiani and David J. Turner and Timothy Aaron Wiles},
  journal={The Annals of Applied Statistics},
  year={2009},
  volume={3},
  pages={710-730}
}
Determination of the minimum inhibitory concentration (MIC)of a drug that prevents microbial growth is an important step formanaging patients with infections. In this paper we present a novelprobabilistic approach that accurately estimates MICs based on apanel of multiple curves reflecting features of bacterial growth. Wedevelop a probabilistic model for determining whether a given dilu-tion of an antimicrobial agent is the MIC given features of the growthcurves over time. Because of the… 
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