Critical Care

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

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Subcategories: Sepsis
Showing 85-105 of 7,402 articles
Quality of Human Expert versus Large Language Model Generated Multiple Choice Questions in the Field of Mechanical Ventilation.

BACKGROUND: Mechanical ventilation (MV) is a critical competency in critical care training, yet stan...

Machine learning for the prediction of augmented renal clearance (ARC) in patients with sepsis in critical care units.

This study aims to establish and validate prediction models based on novel machine learning (ML) alg...

A multimodal dataset for training deep learning models aimed at detecting and analyzing sleep apnea.

Sleep Apnea Syndrome (SAS) is a serious respiratory disorder that can lead to a range of complicatio...

The design of copper flotation process based on multi-label classification and regression.

The intelligent design of copper flotation processes is an important means for improving resource ut...

Modelling Mutagenicity Using Multi-Task Deep Learning and REACH Data.

Under REACH, mutagenicity assessment relies on testing (gene mutation test in bacteria and/or mamma...

AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients.

With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and...

Development of a machine learning-derived model to predict unplanned ICU admissions after major non-cardiac surgery.

BACKGROUND: Unplanned postoperative intensive care unit admissions (UIAs) are rare events that cause...

From omics to AI-mapping the pathogenic pathways in type 2 diabetes.

Understanding the biochemical pathways and interorgan cross talk underlying type 2 diabetes (T2D) is...

Multi-component metabolite electrochemical detection and analysis based on machine learning.

Metabolic molecules are highly correlated with various physiological indicators and diseases, so it ...

On-Mask Magnetoelastic Sensor Network for Self-Powered Respiratory Monitoring.

Respiratory monitoring is crucial because it provides key insights into a person's health and physio...

Multi-tissue Methylation Analysis of Alzheimer's Disease: Insights into Pathways, Modules, and Key Genes.

DNA methylation plays a crucial role in the onset and progression of Alzheimer's disease (AD). Genom...

Deep multi-task learning framework for gastrointestinal lesion-aided diagnosis and severity estimation.

Accurate diagnosis and severity estimation of gastrointestinal tract (GT) lesions are crucial for pa...

Enhancing pathological feature discrimination in diabetic retinopathy multi-classification with self-paced progressive multi-scale training.

Diabetic retinopathy (DR) is a common diabetes complication that presents significant diagnostic cha...

Optimization of a multi-environmental detection model for tomato growth point buds based on multi-strategy improved YOLOv8.

Tomato growing points and flower buds serve as vital physiological indicators influencing yield qual...

Automatic segmentation of liver structures in multi-phase MRI using variants of nnU-Net and Swin UNETR.

Accurate segmentation of the liver parenchyma, portal veins, hepatic veins, and lesions from MRI is ...

Multi-View Fused Nonnegative Matrix Completion Methods for Drug-Target Interaction Prediction.

Accurate prediction of drug-target interactions (DTIs) is crucial for accelerating drug discovery an...

VGRF Signal-Based Gait Analysis for Parkinson's Disease Detection: A Multi-Scale Directed Graph Neural Network Approach.

Parkinson's Disease (PD) is often characterized by abnormal gait patterns, which can be objectively ...

Graph neural network-tracker: a graph neural network-based multi-sensor fusion framework for robust unmanned aerial vehicle tracking.

Unmanned aerial vehicle (UAV) tracking is a critical task in surveillance, security, and autonomous ...

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