AIMC Topic: Bacteremia

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Assessing patient risk of central line-associated bacteremia via machine learning.

American journal of infection control
BACKGROUND: Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors, there remains a need to accurately predict the risk of CLAB...

Prediction of bacterial and fungal bloodstream infections using machine learning in patients undergoing chemotherapy.

European journal of cancer (Oxford, England : 1990)
PURPOSE: This study aimed to develop a machine learning (ML) model to predict bloodstream infection (BSI) in chemotherapy patients.

Machine Learning Model to Guide Empirical Antimicrobial Therapy in Febrile Neutropenic Patients With Hematologic Malignancies.

Anticancer research
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...

Diagnostic Stewardship of Blood Cultures in the Pediatric ICU Using Machine Learning.

Hospital pediatrics
OBJECTIVE: The medical community recently experienced a severe shortage of blood culture media bottles. Rates of blood stream infection (BSI) among critically ill children are low. We sought to design a machine learning (ML) model able to identify ch...

Prediction of bloodstream infection using machine learning based primarily on biochemical data.

Scientific reports
Early diagnosis of bloodstream infection (BSI) is crucial for informed antibiotic use. This study developed a machine learning approach for early BSI detection using a comprehensive dataset from Rigshospitalet, Denmark (2010-2020). The dataset includ...

Utilising routinely collected clinical data through time series deep learning to improve identification of bacterial bloodstream infections: a retrospective cohort study.

The Lancet. Digital health
BACKGROUND: Blood cultures are the gold standard for diagnosing bacterial bloodstream infections, but test results are only available 24-48 h after sampling. We aimed to develop and evaluate models using health-care data to predict bloodstream infect...

A Machine Learning-Based Risk Score for Prediction of Infective Endocarditis Among Patients With Staphylococcus aureus Bacteremia-The SABIER Score.

The Journal of infectious diseases
BACKGROUND: Early risk assessment is needed to stratify Staphylococcus aureus infective endocarditis (SA-IE) risk among patients with S. aureus bacteremia (SAB) to guide clinical management. The objective of the current study was to develop a novel r...

A Vision on User-Centered Implementation and Evaluation of Explainable AI for Predicting Hospital-Onset Bacteremia.

Studies in health technology and informatics
In recent years, artificial intelligence (AI) has gained momentum in many fields of daily live. In healthcare, AI can be used for diagnosing or predicting illnesses. However, explainable AI (XAI) is needed to ensure that users understand how the algo...

Four Biomarkers-Based Artificial Neural Network Model for Accurate Early Prediction of Bacteremia with Low-level Procalcitonin.

Annals of clinical and laboratory science
OBJECTIVE: Procalcitonin levels above 2.0 ng/mL are associated with a higher risk of severe sepsis. Bacteremia with procalcitonin levels lower than 2.0 ng/mL has not received much attention, and relevant prediction models are lacking. Herein, a panel...