AIMC Topic: Carbapenems

Clear Filters Showing 1 to 10 of 16 articles

Machine learning-based evaluation of risk factors for carbapenem-resistant dissemination in neonatal units.

mSystems
Healthcare-associated infections (HAIs), particularly in neonatal intensive care units (NICUs), pose significant challenges due to neonates' vulnerability and the rapid infection spread. However, risk factors facilitating pathogen persistence and dis...

Machine Learning and DIA Proteomics Reveal New Insights into Carbapenem Resistance Mechanisms in .

Journal of proteome research
The emergence of Carbapenem-resistant (CRKP) represents a major public health concern, primarily driven by its ability to evade a wide range of antibiotics. Despite extensive genomic studies, proteomic insights into antibiotic resistance mechanisms ...

Use of machine learning for real-time antibiotic treatment adjustment in high-risk patients with CRGNB infection.

Computer methods and programs in biomedicine
BACKGROUND: Infections caused by carbapenem resistant gram-negative bacilli (CRGNB) are associated with high mortality and pose a great challenge for clinical treatment. We aim to identify patients at high risk for CRGNB as early as possible and aler...

Predicting carbapenem-resistant Pseudomonas aeruginosa infection risk using XGBoost model and explainability.

Scientific reports
The prevalence and spread of carbapenem-resistant Pseudomonas aeruginosa (CRPA) is a global public health problem. This study aims to identify the risk factors of CRPA infection and construct a machine learning model to provide a prediction tool for ...

A novel approach to antimicrobial resistance: Machine learning predictions for carbapenem-resistant Klebsiella in intensive care units.

International journal of medical informatics
This study was conducted at Kocaeli University Hospital in Turkey and aimed to predict carbapenem-resistant Klebsiella pneumoniae infection in intensive care units using the Extreme Gradient Boosting (XGBoost) algorithm, a form of artificial intellig...

Predictive modeling of mortality in carbapenem-resistant bloodstream infections using machine learning.

Journal of investigative medicine : the official publication of the American Federation for Clinical Research
, a notable drug-resistant bacterium, often induces severe infections in healthcare settings, prompting a deeper exploration of treatment alternatives due to escalating carbapenem resistance. This study meticulously examined clinical, microbiological...

Prediction of carbapenem-resistant gram-negative bacterial bloodstream infection in intensive care unit based on machine learning.

BMC medical informatics and decision making
BACKGROUND: Predicting whether Carbapenem-Resistant Gram-Negative Bacterial (CRGNB) cause bloodstream infection when giving advice may guide the use of antibiotics because it takes 2-5 days conventionally to return the results from doctor's order.

A Pragmatic Machine Learning Model To Predict Carbapenem Resistance.

Antimicrobial agents and chemotherapy
Infection caused by carbapenem-resistant (CR) organisms is a rising problem in the United States. While the risk factors for antibiotic resistance are well known, there remains a large need for the early identification of antibiotic-resistant infecti...

Amplification Curve Analysis: Data-Driven Multiplexing Using Real-Time Digital PCR.

Analytical chemistry
Information about the kinetics of PCR reactions is encoded in the amplification curve. However, in digital PCR (dPCR), this information is typically neglected by collapsing each amplification curve into a binary output (positive/negative). Here, we d...