AIMC Topic: Anti-Infective Agents
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Machine Learning Model to Guide Empirical Antimicrobial Therapy in Febrile Neutropenic Patients With Hematologic Malignancies.
Anticancer research
Jun 1, 2025
BACKGROUND/AIM: Optimal antimicrobial selection for patients with febrile neutropenia (FN) may differ depending on the underlying mechanisms. We aimed to develop a model for predicting the severity of bacteremia in patients with FN and hematologic ma...
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...
AI Methods for Antimicrobial Peptides: Progress and Challenges.
Microbial biotechnology
Jan 1, 2025
Antimicrobial peptides (AMPs) are promising candidates to combat multidrug-resistant pathogens. However, the high cost of extensive wet-lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning...
Machine learning for adverse event prediction in outpatient parenteral antimicrobial therapy: a scoping review.
The Journal of antimicrobial chemotherapy
Dec 2, 2024
OBJECTIVE: This study aimed to conduct a scoping review of machine learning (ML) techniques in outpatient parenteral antimicrobial therapy (OPAT) for predicting adverse outcomes and to evaluate their validation, implementation and potential barriers ...
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...
Machine Learning Prediction of Antimicrobial Peptides.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2022
Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in c...
Artificial Intelligence to Guide Empirical Antimicrobial Therapy-Ready for Prime Time?
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Jun 1, 2021
Predicting outcomes in central venous catheter salvage in pediatric central line-associated bloodstream infection.
Journal of the American Medical Informatics Association : JAMIA
Mar 18, 2021
OBJECTIVE: Central line-associated bloodstream infections (CLABSIs) are a common, costly, and hazardous healthcare-associated infection in children. In children in whom continued access is critical, salvage of infected central venous catheters (CVCs)...
Machine learning approaches for elucidating the biological effects of natural products.
Natural product reports
Mar 4, 2021
Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural pr...
Identifying antimicrobial peptides using word embedding with deep recurrent neural networks.
Bioinformatics (Oxford, England)
Jun 1, 2019
MOTIVATION: Antibiotic resistance constitutes a major public health crisis, and finding new sources of antimicrobial drugs is crucial to solving it. Bacteriocins, which are bacterially produced antimicrobial peptide products, are candidates for broad...
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