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01486nam a2200325Ia 4500 |
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10.3233-SHTI220765 |
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220718s2022 CNT 000 0 und d |
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|a 18798365 (ISSN)
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| 245 |
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|a Using Association Rules in Antimicrobial Resistance in Stone Disease Patients
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| 260 |
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|b NLM (Medline)
|c 2022
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| 856 |
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|z View Fulltext in Publisher
|u https://doi.org/10.3233/SHTI220765
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|a 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.
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|a AMR
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|a Antimicrobial Resistance
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|a Association rules
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|a unsupervised ML
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| 700 |
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|a Anastasiou, A.
|e author
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1 |
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|a Feretzakis, G.
|e author
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|a Kalles, D.
|e author
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|a Katsimperis, S.
|e author
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|a Kofopoulou, S.
|e author
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|a Kosmidis, T.
|e author
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|a Koutsouris, D.
|e author
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|a Loupelis, E.
|e author
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|a Manolitsis, I.
|e author
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|a Skolarikos, A.
|e author
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|a Tzelves, L.
|e author
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|a Varkarakis, I.
|e author
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|a Verykios, V.S.
|e author
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| 773 |
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|t Studies in health technology and informatics
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