Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance

Timely and efficacious antibiotic treatment depends on precise and quick in silico antimicrobial-resistance predictions. Limited treatment choices due to antimicrobial resistance (AMR) highlight the necessity to optimize the available diagnostics. AMR can be explicitly anticipated on the basis of ge...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Antibiotics (Basel) Jg. 11; H. 11; S. 1593
Hauptverfasser:	Yasir, Muhammad, Karim, Asad Mustafa, Malik, Sumera Kausar, Bajaffer, Amal A., Azhar, Esam I.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.11.2022 MDPI
Schlagworte:	Accuracy Algorithms Antibiotics Antimicrobial agents Antimicrobial resistance Bacterial infections Ceftazidime Ciprofloxacin Comparative analysis Data mining Decision trees Drug resistance Drug resistance in microorganisms Gene expression Genomes Genomics Infections Learning algorithms Machine learning Meropenem Multidrug resistance Multidrug resistant organisms Nucleotide sequence Pathogens Patients Performance evaluation Physicians Prediction models Pseudomonas aeruginosa Transcriptomes Transcriptomics Saudi Arabia Taiwan
ISSN:	2079-6382, 2079-6382
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!