AIMC Topic: Antimicrobial Stewardship

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Development of a Machine Learning Model Using Electronic Health Record Data to Identify Antibiotic Use Among Hospitalized Patients.

JAMA network open Mar 1, 2021
IMPORTANCE: Comparisons of antimicrobial use among hospitals are difficult to interpret owing to variations in patient case mix. Risk-adjustment strategies incorporating larger numbers of variables haves been proposed as a method to improve compariso...
Male Middle Aged Risk Assessment Aged Young Adult Adult Child Follow-Up Studies Inpatients Antimicrobial Stewardship Infant Adolescent Child, Preschool Humans Machine Learning Electronic Health Records Female Anti-Bacterial Agents Retrospective Studies
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Comprehensive analysis of rule formalisms to represent clinical guidelines: Selection criteria and case study on antibiotic clinical guidelines.

Artificial intelligence in medicine Jan 9, 2020
BACKGROUND: The over-use of antibiotics in clinical domains is causing an alarming increase in bacterial resistance, thus endangering their effectiveness as regards the treatment of highly recurring severe infectious diseases. Whilst Clinical Guideli...
Antimicrobial Stewardship Organizational Case Studies Humans Anti-Bacterial Agents Decision Support Systems, Clinical Practice Guidelines as Topic Cross Infection Urinary Tract Infections Expert Systems
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Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach.

Journal of applied microbiology Nov 12, 2019
AIMS: Analysis and tracking of antimicrobial utilization (AU) are crucial in antimicrobial stewardship efforts which are used to find effective interventions for controlling antimicrobial resistance. In antimicrobial stewardship, standard risk adjust...
Humans Hospitalization Machine Learning Male Anti-Infective Agents Antimicrobial Stewardship Female
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Using natural language processing and VetCompass to understand antimicrobial usage patterns in Australia.

Australian veterinary journal Jun 17, 2019
BACKGROUND: Currently there is an incomplete understanding of antimicrobial usage patterns in veterinary clinics in Australia, but such knowledge is critical for the successful implementation and monitoring of antimicrobial stewardship programs.
Dogs Practice Patterns, Physicians' Antimicrobial Stewardship Records Animals Humans Cats Australia Anti-Infective Agents Veterinarians Natural Language Processing
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Rapid Rule Out of Culture-Negative Bloodstream Infections by Use of a Novel Approach to Universal Detection of Bacteria and Fungi.

The journal of applied laboratory medicine Nov 20, 2018
BACKGROUND: Currently it can take up to 5 days to rule out bloodstream infection. With the low yield of blood cultures (approximately 10%), a significant number of patients are potentially exposed to inappropriate therapy that can lead to adverse eve...
Bacteria Bacteremia Blood Culture Fungemia Anti-Infective Agents Time Factors Real-Time Polymerase Chain Reaction Fungi Fungal Proteins Antimicrobial Stewardship DNA-Directed DNA Polymerase Predictive Value of Tests Limit of Detection Humans Bacterial Proteins
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Comparison of the Abbott Architect BRAHMS and the Biomérieux Vidas BRAHMS Procalcitonin Assays.

The journal of applied laboratory medicine Sep 19, 2018
BACKGROUND: Procalcitonin (PCT) is a well-established marker for bacterial infection. Recently the US Food and Drug Administration approved the expanded use of this biomarker to guide clinical decisions for antibiotic treatment in patients with lower...
Bacterial Infections Clinical Decision-Making Sepsis Limit of Detection Antimicrobial Stewardship Humans Procalcitonin Luminescent Measurements Immunoassay Anti-Bacterial Agents Respiratory Tract Infections Biomarkers
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Accelerating antimicrobial stewardship: An AI-CDSS approach to combating multidrug-resistant pathogens in the era of increasing resistance.

Clinica chimica acta; international journal of clinical chemistry Jun 15, 2025
OBJECTIVES: The World Health Organization has identified Klebsiella pneumoniae (KP) and Pseudomonas aeruginosa (PA) as significant public health threats owing to high antibiotic resistance. Traditional antibiotic susceptibility testing (AST) methods,...
Artificial Intelligence Machine Learning Anti-Bacterial Agents Humans Klebsiella pneumoniae Pseudomonas aeruginosa Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Antimicrobial Stewardship Microbial Sensitivity Tests Drug Resistance, Multiple, Bacterial Decision Support Systems, Clinical
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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...
Electronic Health Records Drug Resistance, Microbial Antimicrobial Stewardship Humans Machine Learning Anti-Bacterial Agents
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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...
Drug Resistance, Microbial Antimicrobial Stewardship Humans Machine Learning Anti-Bacterial Agents Microbial Sensitivity Tests Inpatients Drug Resistance, Bacterial Electronic Health Records
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Algorithmic Stewardship in Health Care.

JAMA Feb 9, 2021
Antimicrobial Stewardship Machine Learning Delivery of Health Care Health Facilities Artificial Intelligence
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