Predictive Modeling of Emerging Antibiotic Resistance Trends

Autor:	Kirthana Beaulac, Elke A. Rundensteiner, Shira Doron, ML Tlachac, Kerri Barton, T. Scott Troppy
Rok vydání:	2019
Předmět:	0301 basic medicine medicine.diagnostic_test Resistance pattern Computer science Model selection 030106 microbiology Antimicrobial susceptibility 030501 epidemiology 03 medical and health sciences Antibiotic resistance Antibiogram medicine Econometrics 0305 other medical science Expansive
Zdroj:	Biomedical Engineering Systems and Technologies ISBN: 9783030291952 BIOSTEC (Selected Papers)
DOI:	10.1007/978-3-030-29196-9_18
Popis:	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 However, these antibiograms provide outdated resistance knowledge.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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::224de8f551d6355de91b9468488d5f40 https://doi.org/10.1007/978-3-030-29196-9_18 Zobrazit plný text záznamu