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| LEADER |
01529nam a2200349Ia 4500 |
| 001 |
10.3233-SHTI220757 |
| 008 |
220718s2022 CNT 000 0 und d |
| 020 |
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|a 18798365 (ISSN)
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| 245 |
1 |
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|a Discovering Association Rules in Antimicrobial Resistance in Intensive Care Unit
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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/SHTI220757
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| 520 |
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|a Multidrug resistant infections in intensive care units represent a worldwide problem with adverse health effects and negative economic implications. As artificial intelligence techniques are increasingly applied in diagnosing, treating, and preventing antimicrobial resistance, in this study, we explore the use of association rule mining in the antibiotic resistance profile of critically ill patients suffering from multidrug resistant infections.
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| 650 |
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|a adult
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| 650 |
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|a antibiotic resistance
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| 650 |
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|a Antimicrobial resistance
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| 650 |
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4 |
|a article
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|a artificial intelligence
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|a Association rules
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| 650 |
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|a critically ill patient
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| 650 |
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|a human
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| 650 |
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|a intensive care unit
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| 650 |
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|a machine learning
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| 650 |
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|a Machine Learning
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| 650 |
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4 |
|a mining
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| 700 |
1 |
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|a Dalainas, I.
|e author
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| 700 |
1 |
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|a Feretzakis, G.
|e author
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| 700 |
1 |
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|a Fildisis, G.
|e author
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| 700 |
1 |
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|a Kalles, D.
|e author
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| 700 |
1 |
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|a Loupelis, E.
|e author
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| 700 |
1 |
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|a Rakopoulou, Z.
|e author
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| 700 |
1 |
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|a Sakagianni, A.
|e author
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| 773 |
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|t Studies in health technology and informatics
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