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Predictive Modeling of Emerging Antibiotic Resistance Trends

  • M. L. TlachacEmail author
  • Elke A. Rundensteiner
  • T. Scott Troppy
  • Kirthana Beaulac
  • Shira Doron
  • Kerri Barton
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1024)

Abstract

Antibiotic resistance is constantly evolving, requiring frequent reevaluation of resistance patterns to guide treatment of bacterial infections. Antibiograms, aggregate antimicrobial susceptibility reports, are critical for evaluating the likelihood of antibiotic effectiveness. However, these antibiograms provide outdated resistance knowledge. Thus, this research employs predictive modeling of historic antibiograms to forecast the current year’s resistance rates. Utilizing subsets of the expansive 15-year Massachusetts statewide antibiogram dataset, we demonstrate the effectiveness of using our model selector PYPER with regression-based models to forecast current antimicrobial susceptibility. A PYPER variant is effective since it leverages the fact that different antibiotic-bacteria-location combinations have different antimicrobial susceptibility trends over time. In addition, we discuss relative weighting of the regression-variant models, the impact of location granularity, and the ability to forecast multiple years into the future.

Keywords

Antimicrobial resistance Antibiograms Regression Support vector regression Autoregressive integrated moving average Model selection 

Notes

Acknowledgements

This work is supported by WPI and the US Department of Education P200A150306: GAANN Fellowships to Support Data-Driven Computing Research. We thank Jian Zou, PhD and Tom Hartvigsen at WPI, Matthew Tlachac at University of Minnesota, and Alfred DeMaria, MD and Monina Klevens, DMD at MDPH for their input on this work. We thank the DSRG community at WPI for providing a stimulating research environment.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • M. L. Tlachac
    • 1
    Email author
  • Elke A. Rundensteiner
    • 1
  • T. Scott Troppy
    • 2
  • Kirthana Beaulac
    • 3
  • Shira Doron
    • 3
  • Kerri Barton
    • 2
  1. 1.Worcester Polytechnic InstituteWorcesterUSA
  2. 2.Massachusetts Department of Public HealthJamaica PlainUSA
  3. 3.Tufts Medical CenterBostonUSA

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