Using Association Rules in Antimicrobial Resistance in Stone Disease Patients-Reference-Cited by-同舟云学术

Using Association Rules in Antimicrobial Resistance in Stone Disease Patients

Published:2022-06-29 Issue: Volume: Page:
ISSN:0926-9630
Container-title:Studies in Health Technology and Informatics
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Author:

Manolitsis Ioannis¹,Feretzakis Georgios²³,Tzelves Lazaros¹,Kalles Dimitris²,Loupelis Evangelos⁴,Katsimperis Stamatis¹,Kosmidis Thanos⁵,Anastasiou Athanasios⁶,Koutsouris Dimitrios⁶,Kofopoulou Stavroula⁶,Verykios Vassilios S.²,Skolarikos Andreas¹,Varkarakis Ioannis¹

Affiliation:

1. Second Department of Urology, National and Kapodistrian University of Athens, Sismanogleio General Hospital, Athens, Greece

2. School of Science and Technology, Hellenic Open University, Patras, Greece

3. Sismanogleio General Hospital, Department of Quality Control, Research and Continuing Education, Marousi, Greece

4. Sismanogleio General Hospital, IT department, Marousi, Greece

5. CareAcross Ltd, London, United Kingdom

6. National Technical University of Athens (NTUA)

Abstract

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.

Publisher

IOS Press

Link

https://ebooks.iospress.nl/pdf/doi/10.3233/SHTI220765

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