Critical Care

Sepsis

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

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Critical-Care Subcategories: Sepsis
Showing 232-252 of 9,027 articles
Large Language Model-Driven Knowledge Graph Construction in Sepsis Care Using Multicenter Clinical Databases: Development and Usability Study.

BACKGROUND: Sepsis is a complex, life-threatening condition characterized by significant heterogenei...

Nanopsychiatry: Advancing psychiatric diagnosis and monitoring through nanotechnology-based detection.

Nanopsychiatry, operating at the nanoscale, leverages engineered nanomaterials and nanodevices to re...

Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines.

OBJECTIVE: Bloodstream infection (BSI) is a significant cause of mortality in patients with hematolo...

Weighted-VAE: A deep learning approach for multimodal data generation applied to experimental T. cruzi infection.

Chagas disease (CD), caused by the protozoan parasite Trypanosoma cruzi (T. cruzi), represents a maj...

Infection and Inflammation in Nuclear Medicine Imaging: The Role of Artificial Intelligence.

Infectious and inflammatory diseases represent a global challenge. Delayed diagnosis and treatment l...

Real-Time Monitoring of Personal Protective Equipment Adherence Using On-Device Artificial Intelligence Models.

Personal protective equipment (PPE) is crucial for infection prevention and is effective only when w...

Machine learning-based prediction of vesicoureteral reflux outcomes in infants under antibiotic prophylaxis.

We aimed to investigate the independent outcome predictors of continuous antibiotic prophylaxis (CAP...

Explainable SHAP-XGBoost models for pressure injuries among patients requiring with mechanical ventilation in intensive care unit.

pressure injuries are significant concern for ICU patients on mechanical ventilation. Early predicti...

Assessment for antibiotic resistance in : A practical and interpretable machine learning model based on genome-wide genetic variation.

() antibiotic resistance poses a global health threat. Accurate identification of antibiotic resist...

Forewarning the seasonal dynamics of corn leafhopper and mollicutes through neural networks.

The corn leafhopper (CL), Dalbulus maidis (DeLong & Wolcott) (Hemiptera: Cicadellidae), has become t...

Comparing large language models for antibiotic prescribing in different clinical scenarios: which performs better?

OBJECTIVES: Large language models (LLMs) show promise in clinical decision-making, but comparative e...

Predicting coronavirus disease 2019 severity using explainable artificial intelligence techniques.

Predictive models for determining coronavirus disease 2019 (COVID-19) severity have been established...

Estimating Periodontal Stability Using Computer Vision.

Periodontitis is a severe infection affecting oral and systemic health and is traditionally diagnose...

Fully automatic categorical analysis of striatal subregions in dopamine transporter SPECT using a convolutional neural network.

OBJECTIVE: To provide fully automatic scanner-independent 5-level categorization of the [I]FP-CIT up...

AI Enhanced explainable early prediction of blood culture positivity in neutropenic patients using clinical and hematologic parameters.

Leukemia patients who receive chemotherapy experience a decline in neutrophils and an increased risk...

SERS-based approaches in the investigation of bacterial metabolism, antibiotic resistance, and species identification.

Surface-enhanced Raman scattering (SERS) is an inelastic scattering phenomenon that occurs when phot...

Interpretable deep learning for deconvolutional analysis of neural signals.

The widespread adoption of deep learning to model neural activity often relies on "black-box" approa...

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