Antimicrobial Resistance and Machine Learning: Challenges and Opportunities-Reference-Cited by-同舟云学术

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Antimicrobial Resistance and Machine Learning: Challenges and Opportunities

Published:2022 Issue: Volume:10 Page:31561-31577
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Elyan Eyad¹^ORCID,Hussain Amir²,Sheikh Aziz³,Elmanama Abdelraouf A.⁴^ORCID,Vuttipittayamongkol Pattaramon⁵^ORCID,Hijazi Karolin⁶

Affiliation:

1. School of Computing, Robert Gordon University, Aberdeen, U.K.

2. School of Computing, Edinburgh Napier University, Edinburgh, U.K.

3. Usher Institute, University of Edinburgh, Edinburgh, U.K.

4. Medical Technology Department, Faculty of Health Sciences, Islamic University of Gaza, Gaza, Palestine

5. School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand

6. School of Medicine, Medical Sciences & Nutrition, University of Aberdeen, Aberdeen, U.K.

Funder

Global Challenge Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09737114.pdf?arnumber=9737114

Reference118 articles.

1. Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal Salmonella

2. A Multinational Survey of Risk Factors for Infection with Extended‐Spectrum β‐Lactamase–Producing Enterobacteriaceae in Nonhospitalized Patients

3. Inpatient management of community-acquired pneumonia at the European Gaza Hospital: a clinical audit

4. Susceptibility Patterns inPseudomonas aeruginosaCausing Nosocomial Infections

5. Antibiotic Resistance of Bacteria Associated with Community-Acquired Urinary Tract Infections in the Southern Area of the Gaza Strip

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2. Microbial community succession and antibiotic resistance gene response during the one-step startup of thermophilic UASB;Journal of Environmental Chemical Engineering;2024-10

3. Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance;Chemistry – An Asian Journal;2024-08-23

4. Meta-analysis of wastewater microbiome for antibiotic resistance profiling;Journal of Microbiological Methods;2024-08

5. Harnessing Machine Learning to Uncover Hidden Patterns in Azole-Resistant CYP51/ERG11 Proteins;Microorganisms;2024-07-25

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