Anomalous Antimicrobial Susceptibility Trend Identification
August 27, 2020
M L Tlachac, Elke Rundensteiner, Kerri Barton, T Scott Troppy, Kirthana Beaulac, Shira Doron
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5880-5883.
PMID: 33019312 | DOI: 10.1109/EMBC44109.2020.9176277
Abstract
Antibiotic resistant bacterial infections are a growing global health crisis. Antibiograms, aggregate antimicrobial resistance reports, are critical for tracking antibiotic susceptibility and prescribing antibiotics. This research leverages fifteen years of the expansive Massachusetts statewide antibiogram dataset curated by the Massachusetts Department of Public Health. Given the lengthy annual antibiogram creation process, data are not timely. Our prior research involved forecasting the current antimicrobial susceptibility given historic antibiograms. The objective for this research is to expand upon this prior work by identifying which antibiotic-bacteria combinations have resistance trends that are not well forecasted. For that, our proposed Previous Year Anomalous Trend Identification (PYATI) strategy employs a cluster driven outlier detection solution to identify the trends to remove before forecasting. Employing PYATI to remove antibiotic-bacteria combinations with anomalous trends statistically significantly reduces the forecasting error for the remaining combinations. As antibiotic resistance is furthered by prescribing ineffective antibiotics, PYATI can be leveraged to improve antibiotic prescribing.