Summary of the main features of the public dataset.

Summary of the main features of the public dataset.

<p>The following antimicrobial agents included in the dataset have been considered in our study: <i>amoxicillin clavulanic acid</i> (AMC), <i>cefepime</i> (FEP), <i>ceftazidime</i> (CAZ), <i>imipenem</i> (IMI), <i>aztreonam</i> (AZT),...

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Bibliographic Details
Main Author:	Chiara Condorelli (19697665) (author)
Other Authors:	Emanuele Nicitra (19697668) (author), Nicolò Musso (6101027) (author), Dafne Bongiorno (14330361) (author), Stefania Stefani (188822) (author), Lucia Valentina Gambuzza (19697671) (author), Vincenza Carchiolo (17797569) (author), Mattia Frasca (19697674) (author)
Published:	2024
Subjects:	Medicine Microbiology Pharmacology Biotechnology Infectious Diseases Virology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified making infections challenging klebsiella pneumoniae </ div >< p predicting antibiotic resistance drug resistance heightens antimicrobial drugs ineffective predict antimicrobial resistance antimicrobial resistance occurs treat infectious diseases machine learning techniques bacterial genomic data resistance renders antibiotics antimicrobial resistance machine learning genomic data support clinicians severe illness pathogen genomes medications employed longer respond genome composition gene content even thousands disease spread containing hundreds
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