AIMC Topic: Drug Resistance, Microbial
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Supervised learning for infection risk inference using pathology data.
BMC medical informatics and decision making
Dec 8, 2017
BACKGROUND: Antimicrobial Resistance is threatening our ability to treat common infectious diseases and overuse of antimicrobials to treat human infections in hospitals is accelerating this process. Clinical Decision Support Systems (CDSSs) have been...
Harnessing advances in mechanisms, detection, and strategies to combat antimicrobial resistance.
The Science of the total environment
Jun 20, 2025
Antimicrobial resistance (AMR) is a growing global health crisis, threatening the effectiveness of antibiotics and other antimicrobial agents, leading to increased morbidity, mortality, and economic burdens. This review article provides a comprehensi...
Disc Diffusion Reader: an AI-powered potential solution to combat antibiotic resistance in developing countries.
Journal of infection in developing countries
May 31, 2025
INTRODUCTION: Antimicrobial resistance (AMR) is a global health challenge, and antimicrobial susceptibility testing (AST) is vital for guiding treatment. Although widely used, the Kirby-Bauer method depends on skilled interpretation, which can be tim...
argNorm: normalization of antibiotic resistance gene annotations to the Antibiotic Resistance Ontology (ARO).
Bioinformatics (Oxford, England)
May 6, 2025
SUMMARY: Currently available and frequently used tools for annotating antibiotic resistance genes (ARGs) in genomes and metagenomes provide results using inconsistent nomenclature. This makes the comparison of different ARG annotation outputs challen...
Strategies in using artificial intelligence to combat antimicrobial resistance.
Recenti progressi in medicina
Mar 1, 2025
Infectious diseases caused by pathogens resistant to antimicrobial treatments, defined as antimicrobial resistance (AMR), are a serious global health crisis, considered among the main threats to global public health according to the World Health Orga...
Applications of Machine Learning on Electronic Health Record Data to Combat Antibiotic Resistance.
The Journal of infectious diseases
Nov 15, 2024
There is growing excitement about the clinical use of artificial intelligence and machine learning (ML) technologies. Advancements in computing and the accessibility of ML frameworks enable researchers to easily train predictive models using electron...
Highly accurate classification and discovery of microbial protein-coding gene functions using FunGeneTyper: an extensible deep learning framework.
Briefings in bioinformatics
May 23, 2024
High-throughput DNA sequencing technologies decode tremendous amounts of microbial protein-coding gene sequences. However, accurately assigning protein functions to novel gene sequences remain a challenge. To this end, we developed FunGeneTyper, an e...
Enhancing Antibiotic Stewardship: A Machine Learning Approach to Predicting Antibiotic Resistance in Inpatient Care.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 1, 2024
Antibiotics have been crucial in advancing medical treatments, but the growing threat of antibiotic resistance challenges these achievements and emphasizes the need for innovative stewardship strategies. In this study, we developed machine learning m...
Highlights From IDWeek 2023-New Vaccines, Artificial Intelligence, and Antimicrobial Resistance.
JAMA
Dec 5, 2023
Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections.
Annals of medicine
Dec 1, 2023
Antimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. Numerous methods are used to overcome this problem with moderate success. Besides efforts of antimicr...
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