Critical Care

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

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Subcategories: Sepsis
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MMGCN: Multi-modal multi-view graph convolutional networks for cancer prognosis prediction.

BACKGROUND AND OBJECTIVE: Accurate prognosis prediction for cancer patients plays a significant role...

Machine learning-based risk prediction model construction of difficult weaning in ICU patients with mechanical ventilation.

In intensive care unit (ICU) patients undergoing mechanical ventilation (MV), the occurrence of diff...

A coordinated adaptive multiscale enhanced spatio-temporal fusion network for multi-lead electrocardiogram arrhythmia detection.

The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of ca...

Predicting Mortality in Sepsis-Associated Acute Respiratory Distress Syndrome: A Machine Learning Approach Using the MIMIC-III Database.

BackgroundTo develop and validate a mortality prediction model for patients with sepsis-associated A...

Deep learning method with integrated invertible wavelet scattering for improving the quality ofcardiac DTI.

Respiratory motion, cardiac motion and inherently low signal-to-noise ratio (SNR) are major limitati...

Timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine Learning Techniques with Readily Available Vital Sign Data.

The ICU is a specialized hospital department that offers critical care to patients at high risk. The...

H-Net: Heterogeneous Neural Network for Multi-Classification of Neuropsychiatric Disorders.

Clinical studies have proved that both structural magnetic resonance imaging (sMRI) and functional m...

A Contrastive-Learning-Based Deep Neural Network for Cancer Subtyping by Integrating Multi-Omics Data.

BACKGROUND: Accurate identification of cancer subtypes is crucial for disease prognosis evaluation a...

Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation.

BACKGROUND: Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essen...

A machine learning-based prediction of hospital mortality in mechanically ventilated ICU patients.

BACKGROUND: Mechanical ventilation (MV) is vital for critically ill ICU patients but carries signifi...

A multi-task deep learning approach for real-time view classification and quality assessment of echocardiographic images.

High-quality standard views in two-dimensional echocardiography are essential for accurate cardiovas...

Mammography classification with multi-view deep learning techniques: Investigating graph and transformer-based architectures.

The potential and promise of deep learning systems to provide an independent assessment and relieve ...

Assessment of multi-modal magnetic resonance imaging for glioma based on a deep learning reconstruction approach with the denoising method.

BACKGROUND: Deep learning reconstruction (DLR) with denoising has been reported as potentially impro...

MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.

Drug therapy remains the primary approach to treating tumours. Variability among cancer patients, in...

Fusion of convolutional neural network with XGBoost feature extraction for predicting multi-constituents in corn using near infrared spectroscopy.

Near-infrared (NIR) spectroscopy has been widely utilized to predict multi-constituents of corn in a...

Deep unfolding network with spatial alignment for multi-modal MRI reconstruction.

Multi-modal Magnetic Resonance Imaging (MRI) offers complementary diagnostic information, but some m...

SG-Fusion: A swin-transformer and graph convolution-based multi-modal deep neural network for glioma prognosis.

The integration of morphological attributes extracted from histopathological images and genomic data...

Early prognosis prediction for non-variceal upper gastrointestinal bleeding in the intensive care unit: based on interpretable machine learning.

INTRODUCTION: This study aims to construct a mortality prediction model for patients with non-varice...

Cross-view discrepancy-dependency network for volumetric medical image segmentation.

The limited data poses a crucial challenge for deep learning-based volumetric medical image segmenta...

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