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Antimicrobial Stewardship

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Explainable and Interpretable Machine Learning for Antimicrobial Stewardship: Opportunities and Challenges.

Clinical therapeutics
There is growing interest in exploiting the advances in artificial intelligence and machine learning (ML) for improving and monitoring antimicrobial prescriptions in line with antimicrobial stewardship principles. Against this background, the concept...

Machine-learning-based risk assessment tool to rule out empirical use of ESBL-targeted therapy in endemic areas.

The Journal of hospital infection
BACKGROUND: Antimicrobial stewardship focuses on identifying patients who require extended-spectrum beta-lactamase (ESBL)-targeted therapy. 'Rule-in' tools have been researched extensively in areas of low endemicity; however, such tools are inadequat...

Navigating the future: machine learning's role in revolutionizing antimicrobial stewardship and infection prevention and control.

Current opinion in infectious diseases
PURPOSE OF REVIEW: This review examines the current state and future prospects of machine learning (ML) in infection prevention and control (IPC) and antimicrobial stewardship (ASP), highlighting its potential to transform healthcare practices by enh...

MAPRS: An intelligent approach for post-prescription review based on multi-label learning.

Artificial intelligence in medicine
Antimicrobial resistance (AMR) is a major threat to public health worldwide. It is a promising way to improve appropriate prescription by the review and stewardship of antimicrobials, and Post-Prescription Review (PPR) is currently the main tool used...

Retrospective validation study of a machine learning-based software for empirical and organism-targeted antibiotic therapy selection.

Antimicrobial agents and chemotherapy
UNLABELLED: Errors in antibiotic prescriptions are frequent, often resulting from the inadequate coverage of the infection-causative microorganism. The efficacy of iAST, a machine-learning-based software offering empirical and organism-targeted antib...

Applications of Machine Learning on Electronic Health Record Data to Combat Antibiotic Resistance.

The Journal of infectious diseases
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...

The role of artificial intelligence in the diagnosis, imaging, and treatment of thoracic empyema.

Current opinion in pulmonary medicine
PURPOSE OF REVIEW: The management of thoracic empyema is often complicated by diagnostic delays, recurrence, treatment failures and infections with antibiotic resistant bacteria. The emergence of artificial intelligence (AI) in healthcare, particular...

External validation of predictive models for antibiotic susceptibility of urine culture.

BJU international
OBJECTIVE: To develop, externally validate, and test a series of computer algorithms to accurately predict antibiotic susceptibility test (AST) results at the time of clinical diagnosis, up to 3 days before standard urine culture results become avail...

Artificial intelligence in antimicrobial stewardship: a systematic review and meta-analysis of predictive performance and diagnostic accuracy.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology
The increasing threat of antimicrobial resistance has prompted a need for more effective antimicrobial stewardship programs (AMS). Artificial intelligence (AI) and machine learning (ML) tools have emerged as potential solutions to enhance decision-ma...

Artificial intelligence-driven approaches in antibiotic stewardship programs and optimizing prescription practices: A systematic review.

Artificial intelligence in medicine
Antimicrobial stewardship programs (ASPs) are essential in optimizing the use of antibiotics to address the global concern of antimicrobial resistance (AMR). Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools for e...