Using Association Rules in Antimicrobial Resistance in Stone Disease Patients

Using Association Rules in Antimicrobial Resistance in Stone Disease Patients

Association rule mining is a very popular unsupervised machine learning technique for discovering patterns in large datasets. Patients with stone disease commonly suffer from urinary tract infections (UTI), complicated by the emergence of antimicrobial resistance (AMR), due to the excessive use of a...

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Bibliographic Details
Main Authors: Anastasiou, A. (Author), Feretzakis, G. (Author), Kalles, D. (Author), Katsimperis, S. (Author), Kofopoulou, S. (Author), Kosmidis, T. (Author), Koutsouris, D. (Author), Loupelis, E. (Author), Manolitsis, I. (Author), Skolarikos, A. (Author), Tzelves, L. (Author), Varkarakis, I. (Author), Verykios, V.S (Author)
Format: Article
Language:English
Published: NLM (Medline) 2022
Subjects:
AMR
Antimicrobial Resistance
Association rules
unsupervised ML
Online Access:View Fulltext in Publisher
Description
Summary:Association rule mining is a very popular unsupervised machine learning technique for discovering patterns in large datasets. Patients with stone disease commonly suffer from urinary tract infections (UTI), complicated by the emergence of antimicrobial resistance (AMR), due to the excessive use of antibiotics. In this study, we explore the use of association rule mining in the AMR profile of patients suffering from stone disease.
ISBN:18798365 (ISSN)
DOI:10.3233/SHTI220765

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