Critical Care

Latest AI and machine learning research in critical care for healthcare professionals.

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Subcategories: Sepsis
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Multi-task heterogeneous graph learning on electronic health records.

Learning electronic health records (EHRs) has received emerging attention because of its capability ...

PhosBERT: A self-supervised learning model for identifying phosphorylation sites in SARS-CoV-2-infected human cells.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus, which m...

Skeleton-guided multi-scale dual-coordinate attention aggregation network for retinal blood vessel segmentation.

Deep learning plays a pivotal role in retinal blood vessel segmentation for medical diagnosis. Despi...

Neural network analysis of pharyngeal sounds can detect obstructive upper respiratory disease in brachycephalic dogs.

Brachycephalic obstructive airway syndrome (BOAS) is a highly prevalent respiratory disease affectin...

Explainable machine learning for assessing upper respiratory tract of racehorses from endoscopy videos.

Laryngeal hemiplegia (LH) is a major upper respiratory tract (URT) complication in racehorses. Endos...

Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses.

The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical...

Deep learning and optimization enabled multi-objective for task scheduling in cloud computing.

In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution....

MASDF-Net: A Multi-Attention Codec Network with Selective and Dynamic Fusion for Skin Lesion Segmentation.

Automated segmentation algorithms for dermoscopic images serve as effective tools that assist dermat...

Improving Xenon-129 lung ventilation image SNR with deep-learning based image reconstruction.

PURPOSE: To evaluate the feasibility and utility of a deep learning (DL)-based reconstruction for im...

Finite-time cluster synchronization of multi-weighted fractional-order coupled neural networks with and without impulsive effects.

In this paper, finite-time cluster synchronization (FTCS) of multi-weighted fractional-order neural ...

Prediction of sepsis mortality in ICU patients using machine learning methods.

PROBLEM: Sepsis, a life-threatening condition, accounts for the deaths of millions of people worldwi...

The application of blood flow sound contrastive learning to predict arteriovenous graft stenosis of patients with hemodialysis.

End-stage kidney disease (ESKD) presents a significant public health challenge, with hemodialysis (H...

Distinguishing neonatal culture-negative sepsis from rule-out sepsis with artificial intelligence-derived graphs.

Novel artificial intelligence methods can aide in identification of cases of conditions using only u...

Predictive Models of Long-Term Outcome in Patients with Moderate to Severe Traumatic Brain Injury are Biased Toward Mortality Prediction.

BACKGROUND: The prognostication of long-term functional outcomes remains challenging in patients wit...

Considering multi-scale built environment in modeling severity of traffic violations by elderly drivers: An interpretable machine learning framework.

The causes of traffic violations by elderly drivers are different from those of other age groups. To...

Machine Learning Tools for Acute Respiratory Distress Syndrome Detection and Prediction.

Machine learning (ML) tools for acute respiratory distress syndrome (ARDS) detection and prediction ...

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