AIMC Topic: Urinary Tract Infections

Clear Filters Showing 1 to 10 of 63 articles

Orthogonal Biochemical Sensing for Concentration-Independent Bacterial Fingerprinting.

Analytical chemistry
Array-based biosensors hold substantial promise for rapid bacterial identification. However, conventional approaches face two key limitations: their reliance on nonspecific interactions with bacterial surfaces hinders biochemical interpretation, and ...

Exploring multidrug resistance patterns in community-acquired urinary tract infections with machine learning.

Antimicrobial agents and chemotherapy
While associations of antibiotic resistance traits are not random in multidrug-resistant (MDR) bacteria, clinically relevant resistance patterns remain underexplored. This study used association-set mining to explore resistance associations within i...

Machine learning to predict bacteriuria in the emergency department.

Scientific reports
Urinary tract infections (UTIs) are among the most common bacterial infections, yet they are both frequently misdiagnosed and inappropriately treated. We aimed to determine whether a machine learning model could accurately predict bacteriuria by usin...

Aptamer-functionalized graphene quantum dots combined with artificial intelligence detect bacteria for urinary tract infections.

Frontiers in cellular and infection microbiology
OBJECTIVES: Urinary tract infection is one of the most prevalent forms of bacterial infection, and prompt and efficient identification of pathogenic bacteria plays a pivotal role in the management of urinary tract infections. In this study, we propos...

Point-of-Care Sensors and Medical Internet-of-Things Technologies to Manage Catheter-Associated Urinary Tract Infections in the Intensive Care Unit.

Critical care nursing clinics of North America
This article examines the current technologies used with indwelling urinary catheters to monitor potential catheter-associated urinary tract infections (CAUTIs) in the intensive care unit. Advancements in medical internet-of-things, artificial intell...

Machine learning-based prediction of vesicoureteral reflux outcomes in infants under antibiotic prophylaxis.

Scientific reports
We aimed to investigate the independent outcome predictors of continuous antibiotic prophylaxis (CAP) in vesicoureteral reflux, train a model to predict the outcome, and evaluate which infants should be referred for endoscopic vesicoureteral reflux c...

Predicting 90-day risk of urinary tract infections following urostomy in bladder cancer patients using machine learning and explainability.

Scientific reports
This research aims to design and validate a machine learning model to predict the probability of urinary tract infections within 90 days post-urostomy in bladder cancer patients. Clinical and follow-up information from 317 patients who had urostomy p...

Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables.

BMC medical informatics and decision making
BACKGROUND: Urinary tract infection (UTI) is a frequent health-threatening condition. Early reliable diagnosis of UTI helps to prevent misuse or overuse of antibiotics and hence prevent antibiotic resistance. The gold standard for UTI diagnosis is ur...

Use of Machine Learning to Assess the Management of Uncomplicated Urinary Tract Infection.

JAMA network open
IMPORTANCE: Uncomplicated urinary tract infection (UTI) is a common indication for outpatient antimicrobial therapy. National guidelines for the management of uncomplicated UTI were published in 2011, but the extent to which they align with current p...

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...