AIMC Topic: Blood Culture

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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...
Fungi Fungal Proteins Bacteria Fungemia Antimicrobial Stewardship Bacteremia Limit of Detection DNA-Directed DNA Polymerase Bacterial Proteins Blood Culture Humans Predictive Value of Tests Anti-Infective Agents Time Factors Real-Time Polymerase Chain Reaction
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Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks.

Artificial intelligence in medicine Nov 9, 2018
INTRODUCTION: Blood cultures are often performed in the intensive care unit (ICU) to detect bloodstream infections and identify pathogen type, further guiding treatment. Early detection is essential, as a bloodstream infection can give cause to sepsi...
Intensive Care Units Electronic Health Records Blood Culture Humans Neural Networks, Computer
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Automated Interpretation of Blood Culture Gram Stains by Use of a Deep Convolutional Neural Network.

Journal of clinical microbiology Feb 22, 2018
Microscopic interpretation of stained smears is one of the most operator-dependent and time-intensive activities in the clinical microbiology laboratory. Here, we investigated application of an automated image acquisition and convolutional neural net...
Deep Learning Big Data Microscopy Image Interpretation, Computer-Assisted Blood Culture Reproducibility of Results Sensitivity and Specificity Algorithms Phenazines Humans Automation, Laboratory Gentian Violet Neural Networks, Computer
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Diagnostic Stewardship of Blood Cultures in the Pediatric ICU Using Machine Learning.

Hospital pediatrics Jun 1, 2025
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...
Infant Adolescent Female Male Intensive Care Units, Pediatric Machine Learning Bacteremia Retrospective Studies Blood Culture Humans Child, Preschool Child
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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 Mar 1, 2025
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...
Retrospective Studies Female Aged Humans Biomarkers Middle Aged Deep Learning Bacteremia Adult Blood Culture Male
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The Development and Validation of a Machine Learning Model to Predict Bacteremia and Fungemia in Hospitalized Patients Using Electronic Health Record Data.

Critical care medicine Nov 1, 2020
OBJECTIVES: Bacteremia and fungemia can cause life-threatening illness with high mortality rates, which increase with delays in antimicrobial therapy. The objective of this study is to develop machine learning models to predict blood culture results ...
Models, Statistical Fungemia Bacteremia Blood Culture Risk Factors Hospitalization Male Middle Aged Aged Female Reproducibility of Results Retrospective Studies Humans Electronic Health Records Machine Learning
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