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Materials for paper titled 'Predicting Future Antibiotic Susceptibility using Regression-based Methods on Longitudinal Massachusetts Antibiogram Data'

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HEALTHINF2018

Materials for paper titled 'Predicting Future Antibiotic Susceptibility using Regression-based Methods on Longitudinal Massachusetts Antibiogram Data' (https://pdfs.semanticscholar.org/541f/899f6df26852a85bb8dba9961d46e34cf275.pdf).

Input data is a csv file containing columns for "component", "organism", "Report Year", "Total Tests (by organism)", "Indicator Value (Pct)", "Patients", "hopitalid", and "countyName". The types respectively are string, string, integer, integer, integer, string, integer, string. See the paper for more information regarding the meaning of these column names.

Citation:

Tlachac, M. L., Rundensteiner, E. A., Barton, K., Troppy, S., Beaulac, K., & Doron, S. (2018). Predicting Future Antibiotic Susceptibility using Regression-based Methods on Longitudinal Massachusetts Antibiogram Data. In HEALTHINF (pp. 103-114).

@inproceedings{tlachac2018predicting,
  title={Predicting Future Antibiotic Susceptibility using Regression-based Methods on Longitudinal Massachusetts Antibiogram Data.},
  author={Tlachac, ML and Rundensteiner, Elke A and Barton, Kerri and Troppy, Scott and Beaulac, Kirthana and Doron, Shira},
  booktitle={HEALTHINF},
  pages={103--114},
  year={2018}
}

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Materials for paper titled 'Predicting Future Antibiotic Susceptibility using Regression-based Methods on Longitudinal Massachusetts Antibiogram Data'

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