Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach

Aims Analysis and tracking of antimicrobial utilization (AU) are crucial in antimicrobial stewardship efforts which are used to find effective interventions for controlling antimicrobial resistance. In antimicrobial stewardship, standard risk adjustment models are needed for benchmarking appropriate...

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Veröffentlicht in:	Journal of applied microbiology Jg. 128; H. 3; S. 688 - 696
Hauptverfasser:	Chowdhury, A.S., Lofgren, E.T., Moehring, R.W., Broschat, S.L.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Oxford University Press 01.03.2020
Schlagworte:	Algorithms Anti-Infective Agents - therapeutic use antibiotic resistance Antibiotics Antimicrobial agents Antimicrobial resistance Antimicrobial Stewardship antimicrobial utilization artificial intelligence Benchmarks cubist regression data imputation Drug resistance Female Hospitalization hospitals Humans Learning algorithms Machine Learning Male patient features Patients Regression analysis Risk risk adjustment Southeastern United States standardized antimicrobial administration ratio Statistical analysis Support vector machines support vector regression therapeutics Southeastern United States support vector regression data imputation standardized antimicrobial administration ratio antimicrobial stewardship antimicrobial resistance patient features antimicrobial utilization machine learning cubist regression
ISSN:	1364-5072, 1365-2672, 1365-2672
Online-Zugang:	Volltext
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