Critical Care

Sepsis

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

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Critical-Care Subcategories: Sepsis
Showing 337-357 of 9,027 articles
Interpretable machine learning-based prediction of 28-day mortality in ICU patients with sepsis: a multicenter retrospective study.

BACKGROUND: Sepsis is a major cause of mortality in intensive care units (ICUs) and continues to pos...

ARGai 1.0: A GAN augmented in silico approach for identifying resistant genes and strains in E. coli using vision transformer.

The emergence of infectious disease and antibiotic resistance in bacteria like Escherichia coli (E. ...

Plasma Epstein-Barr Virus DNA load for diagnostic and prognostic assessment in intestinal Epstein-Barr Virus infection.

BACKGROUND: The prospective application of plasma Epstein-Barr virus (EBV) DNA load as a noninvasive...

Isolation, identification and characteristics of from diseased rainbow trout ().

is an opportunistic pathogen that can infect humans, animals and aquatic species, which is widely d...

Effects of antibiotic therapy on the early development of gut microbiota and butyrate-producers in early infants.

BACKGROUND: Antibiotics, as the most commonly prescribed class of drugs in neonatal intensive care u...

Interpretable machine learning for predicting sepsis risk in emergency triage patients.

The study aimed to develop and validate a sepsis prediction model using structured electronic medica...

Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators.

The long-term presence of antibiotics in the aquatic environment will affect ecology and human healt...

Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients.

BACKGROUND: Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing ...

Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring.

Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and...

Trace detection of antibiotics in wastewater using tunable core-shell nanoparticles SERS substrate combined with machine learning algorithms.

Surface-enhanced Raman scattering (SERS) show great potential for rapid and highly sensitive detecti...

Bioequivalence study of fluticasone propionate nebuliser suspensions in healthy Chinese subjects.

BACKGROUND: Fluticasone propionate is a synthetic trifluoro-substituted glucocorticoid, a highly sel...

Machine Learning-Enhanced Bacteria Detection Using a Fluorescent Sensor Array with Functionalized Graphene Quantum Dots.

Pathogenic bacteria are the source of many serious health problems, such as foodborne diseases and h...

Use of Machine Learning to Assess the Management of Uncomplicated Urinary Tract Infection.

IMPORTANCE: Uncomplicated urinary tract infection (UTI) is a common indication for outpatient antimi...

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to purine metabolism-associated genes.

Sepsis, characterized as a systemic inflammatory response triggered by pathogen invasion, represents...

Functional antimicrobial peptide-loaded 3D scaffolds for infected bone defect treatment with AI and multidimensional printing.

Infection is the most prevalent complication of fractures, particularly in open fractures, and often...

Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study.

BACKGROUND: Sepsis remains a significant challenge in patients with major trauma in the ICU. Early d...

Quantification of L-lactic acid in human plasma samples using Ni-based electrodes and machine learning approach.

This work presents a robust strategy for quantifying overlapping electrochemical signatures originat...

Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.

BACKGROUND: Patients with Gram-negative bloodstream infections are at risk of serious adverse outcom...

Identification of sepsis-associated encephalopathy biomarkers through machine learning and bioinformatics approaches.

Sepsis-associated encephalopathy (SAE) is common in septic patients, characterized by acute and long...

Deep learning on CT scans to predict checkpoint inhibitor treatment outcomes in advanced melanoma.

Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is asso...

Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper.

Antibiotic resistance is a global public health concern. Bacteria have evolved resistance to most an...

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