Critical Care

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

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Multiple Embeddings Enhanced Multi-Graph Neural Networks for Chinese Healthcare Named Entity Recognition.

Named Entity Recognition (NER) is a natural language processing task for recognizing named entities ...

Assessing the Adequacy of Hemodialysis Patients via the Graph-Based Takagi-Sugeno-Kang Fuzzy System.

Maintenance hemodialysis is the main method for the treatment of end-stage renal disease in China. T...

MSDS-UNet: A multi-scale deeply supervised 3D U-Net for automatic segmentation of lung tumor in CT.

Lung cancer is one of the most common and deadly malignant cancers. Accurate lung tumor segmentation...

Low-Light Image Enhancement Based on Multi-Path Interaction.

Due to the non-uniform illumination conditions, images captured by sensors often suffer from uneven ...

Use of neurally adjusted ventilatory assist (NAVA) in a patient with severe SARS-CoV-2 pneumonia: A case report.

INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia may necessitate...

A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram.

Interictal epileptiform discharges (IEDs) are an important and widely accepted biomarker used in the...

Learning Deep Global Multi-Scale and Local Attention Features for Facial Expression Recognition in the Wild.

Facial expression recognition (FER) in the wild received broad concerns in which occlusion and pose ...

A model of modified -iodobenzylguanidine conjugated gold nanoparticles for neuroblastoma treatment.

Iodine-131 -iodobenzylguanidine (I-IBG) has been utilized as a standard treatment to minimize advers...

Pixel-wise body composition prediction with a multi-task conditional generative adversarial network.

The analysis of human body composition plays a critical role in health management and disease preven...

Three-stage segmentation of lung region from CT images using deep neural networks.

BACKGROUND: Lung region segmentation is an important stage of automated image-based approaches for t...

Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network.

Endometrial cancer (EC) is the most common gynecological tumor in developed countries, and preoperat...

DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data.

Multi-omics data are good resources for prognosis and survival prediction; however, these are diffic...

Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron.

Contactless or non-invasive technology has a significant impact on healthcare applications such as t...

Multi-Modal Adaptive Fusion Transformer Network for the Estimation of Depression Level.

Depression is a severe psychological condition that affects millions of people worldwide. As depress...

Second-order multi-instance learning model for whole slide image classification.

Whole slide histopathology images (WSIs) play a crucial role in diagnosing lymph node metastasis of ...

Fine-grained classification based on multi-scale pyramid convolution networks.

The large intra-class variance and small inter-class variance are the key factor affecting fine-grai...

Robot assisted minimally invasive esophagectomy: safety, perioperative morbidity and short-term oncological outcome-a single institution experience.

Robot assisted minimally invasive esophagectomy (RAMIE) has evolved over the past decade to become p...

Multi-Robot 2.5D Localization and Mapping Using a Monte Carlo Algorithm on a Multi-Level Surface.

Most indoor environments have wheelchair adaptations or ramps, providing an opportunity for mobile r...

Timesias: A machine learning pipeline for predicting outcomes from time-series clinical records.

The prediction of outcomes is a critical part of the clinical surveillance for hospitalized patients...

Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia.

PURPOSE: Comparison of deep learning algorithm, radiomics and subjective assessment of chest CT for ...

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