Critical Care

Sepsis

Latest AI and machine learning research in sepsis for healthcare professionals.

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Critical-Care Subcategories: Sepsis
Showing 568-588 of 9,008 articles
Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model.

AIMS: Although there are various model-based approaches to individualized vancomycin (VCM) administr...

Protein function annotation and virulence factor identification of Klebsiella pneumoniae genome by multiple machine learning models.

Klebsiella pneumoniae is a type of Gram-negative bacterium which can cause a range of infections in ...

Predicting ICU Interventions: A Transparent Decision Support Model Based on Multivariate Time Series Graph Convolutional Neural Network.

In this study, we present a novel approach for predicting interventions for patients in the intensiv...

Discovery of antimicrobial peptides in the global microbiome with machine learning.

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machi...

Random forest differentiation of Escherichia coli in elderly sepsis using biomarkers and infectious sites.

This study addresses the challenge of accurately diagnosing sepsis subtypes in elderly patients, par...

Machine Learning Tools to Assist the Synthesis of Antibacterial Carbon Dots.

INTRODUCTION: The emergence and rapid spread of multidrug-resistant bacteria (MRB) caused by the exc...

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

INTRODUCTION: Despite research efforts, predicting Clostridioides difficile incidence and its outcom...

Navigating the future: machine learning's role in revolutionizing antimicrobial stewardship and infection prevention and control.

PURPOSE OF REVIEW: This review examines the current state and future prospects of machine learning (...

Deep learning assisted logic gates for real-time identification of natural tetracycline antibiotics.

The overuse and misuse of tetracycline (TCs) antibiotics, including tetracycline (TTC), oxytetracycl...

Fillable Magnetic Microrobots for Drug Delivery to Cardiac Tissues In Vitro.

Many cardiac diseases, such as arrhythmia or cardiogenic shock, cause irregular beating patterns tha...

Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.

A common result of infection is an abnormal immune response, which may be detrimental to the host. T...

A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.

BACKGROUND: Sepsis, an acute and potentially fatal systemic response to infection, significantly imp...

Screening and Identification of Neutrophil Extracellular Trap-related Diagnostic Biomarkers for Pediatric Sepsis by Machine Learning.

Neutrophil extracellular trap (NET) is released by neutrophils to trap invading pathogens and can le...

Interpretable machine learning models for predicting the incidence of antibiotic- associated diarrhea in elderly ICU patients.

BACKGROUND: Antibiotic-associated diarrhea (AAD) can prolong hospitalization, increase medical costs...

Predicting Schistosomiasis Intensity in Africa: A Machine Learning Approach to Evaluate the Progress of WHO Roadmap 2030.

The World Health Organization (WHO) 2030 Roadmap aims to eliminate schistosomiasis as a public healt...

An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury.

Sepsis-Associated Liver Injury (SALI) is an independent risk factor for death from sepsis. The aim o...

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