AIMC Topic: Bacteremia

Clear Filters Showing 21 to 30 of 41 articles

Real-time artificial intelligence system for bacteremia prediction in adult febrile emergency department patients.

International journal of medical informatics Aug 6, 2023
BACKGROUND: Artificial intelligence (AI) holds significant potential to be a valuable tool in healthcare. However, its application for predicting bacteremia among adult febrile patients in the emergency department (ED) remains unclear. Therefore, we ...
Humans Artificial Intelligence Male Middle Aged Bacteremia Logistic Models Emergency Service, Hospital Algorithms Retrospective Studies Female Adult
View on PubMed DOI

Predicting bloodstream infection outcome using machine learning.

Scientific reports Oct 11, 2021
Bloodstream infections (BSI) are a main cause of infectious disease morbidity and mortality worldwide. Early prediction of BSI patients at high risk of poor outcomes is important for earlier decision making and effective patient stratification. We de...
Bacteria Bacteremia Aged Male Sepsis ROC Curve Humans Machine Learning Anti-Bacterial Agents Retrospective Studies Electronic Health Records Female Risk Factors
View on PubMed DOI

Aiding clinical assessment of neonatal sepsis using hematological analyzer data with machine learning techniques.

International journal of laboratory hematology May 4, 2021
INTRODUCTION: Early diagnosis and antibiotic administration are essential for reducing sepsis morbidity and mortality; however, diagnosis remains difficult due to complex pathogenesis and presentation. We created a machine learning model for bacteria...
Hematologic Tests Risk Assessment Bacteremia Humans Logistic Models Machine Learning Neonatal Sepsis Algorithms Infant, Newborn
View on PubMed DOI

Development and utility assessment of a machine learning bloodstream infection classifier in pediatric patients receiving cancer treatments.

BMC cancer Nov 13, 2020
BACKGROUND: Objectives were to build a machine learning algorithm to identify bloodstream infection (BSI) among pediatric patients with cancer and hematopoietic stem cell transplantation (HSCT) recipients, and to compare this approach with presence o...
Male Neoplasms Infant Sepsis Prognosis Retrospective Studies Female Infant, Newborn Support Vector Machine Follow-Up Studies Hematopoietic Stem Cell Transplantation Neutropenia Adolescent Child, Preschool Child Bacteremia Humans Machine Learning
View on PubMed DOI

A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates.

mBio Aug 25, 2020
Variation in the genome of , an important pathogen, can have dramatic impacts on the bacterium's ability to cause disease. We therefore asked whether it was possible to predict the virulence of isolates based on their genomic content. We applied a m...
Pseudomonas Infections Genome, Bacterial Mice, Inbred BALB C Machine Learning Virulence Algorithms Pseudomonas aeruginosa Phenotype Mice Proof of Concept Study Female Genomics Animals Bacteremia
View on PubMed DOI

Detection of Bacteremia in Surgical In-Patients Using Recurrent Neural Network Based on Time Series Records: Development and Validation Study.

Journal of medical Internet research Aug 4, 2020
BACKGROUND: Detecting bacteremia among surgical in-patients is more obscure than other patients due to the inflammatory condition caused by the surgery. The previous criteria such as systemic inflammatory response syndrome or Sepsis-3 are not availab...
Retrospective Studies Female Male Deep Learning Bacteremia Inpatients Humans Neural Networks, Computer
View on PubMed DOI

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...
Limit of Detection Humans Bacteria Antimicrobial Stewardship Bacterial Proteins Anti-Infective Agents Bacteremia DNA-Directed DNA Polymerase Time Factors Blood Culture Fungi Fungemia Real-Time Polymerase Chain Reaction Predictive Value of Tests Fungal Proteins
View on PubMed DOI

Supervised machine learning in critical care: The path forward.

Journal of critical care May 18, 2018
Critical Care Humans Bacteremia Supervised Machine Learning
View on PubMed DOI

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

American journal of infection control Apr 13, 2018
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...
Male Middle Aged Risk Assessment Infant Case-Control Studies Adolescent Child, Preschool Child Aged Humans Aged, 80 and over Young Adult Epidemiologic Methods Machine Learning Bacteremia Adult Retrospective Studies Catheterization, Central Venous Female Catheter-Related Infections Infant, Newborn Academic Medical Centers
View on PubMed DOI

Predicting central line-associated bloodstream infections and mortality using supervised machine learning.

Journal of critical care Feb 21, 2018
PURPOSE: The purpose of this study was to compare machine learning techniques for predicting central line-associated bloodstream infection (CLABSI).
Middle Aged Supervised Machine Learning Risk Assessment Humans Florida Logistic Models Hospital Mortality Databases, Factual Simplified Acute Physiology Score Bacteremia Predictive Value of Tests Intensive Care Units Central Venous Catheters Female Catheter-Related Infections Male
View on PubMed DOI
Popular Topics
Recent Journals