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Bacteremia

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

mBio
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...

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
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...

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

BMC cancer
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...

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
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 ...

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...

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

International journal of laboratory hematology
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...

Predicting bloodstream infection outcome using machine learning.

Scientific reports
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...

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

International journal of medical informatics
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 ...

Predicting community acquired bloodstream infection in infants using full blood count parameters and C-reactive protein; a machine learning study.

European journal of pediatrics
Early recognition of bloodstream infection (BSI) in infants can be difficult, as symptoms may be non-specific, and culture can take up to 48 h. As a result, many infants receive unneeded antibiotic treatment while awaiting the culture results. In thi...

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...