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Clostridium Infections

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Near-infrared spectroscopy coupled with chemometrics algorithms for the quantitative determination of the germinability of Clostridium perfringens in four different matrices.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Clostridium perfringens (C. perfringens) has the ability to form metabolically-dormant spores that can survive food preservation processes and cause food spoilage and foodborne safety risks upon germination outgrowth. This study was conducted to inve...

Estimating Local Costs Associated With Clostridium difficile Infection Using Machine Learning and Electronic Medical Records.

Infection control and hospital epidemiology
BACKGROUND Reported per-patient costs of Clostridium difficile infection (CDI) vary by 2 orders of magnitude among different hospitals, implying that infection control officers need precise, local analyses to guide rational decision making between in...

AI Tackles Hospital Infections: Machine Learning Is Helping Clinicians.

IEEE pulse
For Ashley Zappia (Figure 1), getting her hands dirty was part of her job. Even though she always tried to remain as clean as possible, her work as a nursing aide at a Southern California hospital required a lot of diapering, changing, and other hand...

Modest Clostridiodes difficile infection prediction using machine learning models in a tertiary care hospital.

Diagnostic microbiology and infectious disease
Previous studies have shown promising results of machine learning (ML) models for predicting health outcomes. We develop and test ML models for predicting Clostridioides difficile infection (CDI) in hospitalized patients. This is a retrospective coho...

Anastomotic Leak is Increased With Infection After Colectomy: Machine Learning-Augmented Propensity Score Modified Analysis of 46 735 Patients.

The American surgeon
BACKGROUND: infection (CDI) is now the most common cause of healthcare-associated infections, with increasing prevalence, severity, and mortality of nosocomial and community-acquired CDI which makes up approximately one third of all CDI. There are a...

Machine Learning-Based Prediction Models for Clostridioides difficile Infection: A Systematic Review.

Clinical and translational gastroenterology
INTRODUCTION: Despite research efforts, predicting Clostridioides difficile incidence and its outcomes remains challenging. The aim of this systematic review was to evaluate the performance of machine learning (ML) models in predicting C. difficile i...

Predicting Clostridioides difficile infection outcomes with explainable machine learning.

EBioMedicine
BACKGROUND: Clostridioides difficile infection results in life-threatening short-term outcomes and the potential for subsequent recurrent infection. Predicting these outcomes at diagnosis, when important clinical decisions need to be made, has proven...

Prediction of 28-Day All-Cause Mortality in Heart Failure Patients with Clostridioides difficile Infection Using Machine Learning Models: Evidence from the MIMIC-IV Database.

Cardiology
INTRODUCTION: Heart failure (HF) may induce bowel hypoperfusion, leading to hypoxia of the villa of the bowel wall and the occurrence of Clostridioides difficile infection (CDI). However, the risk factors for the development of CDI in HF patients hav...

Analysis of high-molecular-weight proteins using MALDI-TOF MS and machine learning for the differentiation of clinically relevant Clostridioides difficile ribotypes.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology
PURPOSE: Clostridioides difficile is the main cause of antibiotic related diarrhea and some ribotypes (RT), such as RT027, RT181 or RT078, are considered high risk clones. A fast and reliable approach for C. difficile ribotyping is needed for a corre...