Critical Care

Sepsis

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

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Critical-Care Subcategories: Sepsis
Showing 547-567 of 9,008 articles
Evaluating Explanations From AI Algorithms for Clinical Decision-Making: A Social Science-Based Approach.

Explainable Artificial Intelligence (XAI) techniques generate explanations for predictions from AI m...

Doctors' perception on the ethical use of AI-enabled clinical decision support systems for antibiotic prescribing recommendations in Singapore.

OBJECTIVES: The increased utilization of Artificial intelligence (AI) in healthcare changes practice...

Assessing gait dysfunction severity in Parkinson's Disease using 2-Stream Spatial-Temporal Neural Network.

Parkinson's Disease (PD), a neurodegenerative disorder, significantly impacts the quality of life fo...

Optimal use of β-lactams in neonates: machine learning-based clinical decision support system.

BACKGROUND: Accurate prediction of the optimal dose for β-lactam antibiotics in neonatal sepsis is c...

Discovery of Antimicrobial Lysins from the "Dark Matter" of Uncharacterized Phages Using Artificial Intelligence.

The rapid rise of antibiotic resistance and slow discovery of new antibiotics have threatened global...

DrugSK: A Stacked Ensemble Learning Framework for Predicting Drug Combinations of Multiple Diseases.

Combination therapy is an important direction of continuous exploration in the field of medicine, wi...

Characterization of the prevalence of Salmonella in different retail chicken supply modes using genome-wide and machine-learning analyses.

Salmonella is a foodborne pathogen that causes salmonellosis, of which retail chicken meat is a majo...

A machine learning-based strategy to elucidate the identification of antibiotic resistance in bacteria.

Microorganisms, crucial for environmental equilibrium, could be destructive, resulting in detrimenta...

Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.

Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Findin...

Sepsis mortality prediction with Machine Learning Tecniques.

OBJECTIVE: To develop a sepsis death classification model based on machine learning techniques for p...

Deep-learning-enabled antibiotic discovery through molecular de-extinction.

Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other pres...

Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy.

Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths and 10 m...

Fine-Scale Spatial Prediction on the Risk of Infection in the Republic of Korea.

BACKGROUND: Malaria elimination strategies in the Republic of Korea (ROK) have decreased malaria inc...

Unraveling the genetic and molecular landscape of sepsis and acute kidney injury: A comprehensive GWAS and machine learning approach.

OBJECTIVES: This study aimed to explore the underlying mechanisms of sepsis and acute kidney injury ...

TrueTH: A user-friendly deep learning approach for robust dopaminergic neuron detection.

Parkinson's disease (PD) entails the progressive loss of dopaminergic (DA) neurons in the substantia...

Machine Learning-Based Prediction of Helicobacter pylori Infection Study in Adults.

BACKGROUND Helicobacter pylori has a high infection rate worldwide, and epidemiological study of H. ...

Predictive approach for liberation from acute dialysis in ICU patients using interpretable machine learning.

Renal recovery following dialysis-requiring acute kidney injury (AKI-D) is a vital clinical outcome ...

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