Hospital-Based Medicine

Intensivists

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

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CS-based multi-task learning network for arrhythmia reconstruction and classification using ECG signals.

. Although deep learning-based current methods have achieved impressive results in electrocardiograp...

Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit.

BACKGROUND: Interpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) p...

An Improved Combination of Faster R-CNN and U-Net Network for Accurate Multi-Modality Whole Heart Segmentation.

Detailed information of substructures of the whole heart is usually vital in the diagnosis of cardio...

Multi-Task Distributed Learning Using Vision Transformer With Random Patch Permutation.

The widespread application of artificial intelligence in health research is currently hampered by li...

Multi-modal medical image classification using deep residual network and genetic algorithm.

Artificial intelligence (AI) development across the health sector has recently been the most crucial...

DARMF-UNet: A dual-branch attention-guided refinement network with multi-scale features fusion U-Net for gland segmentation.

Accurate gland segmentation is critical in determining adenocarcinoma. Automatic gland segmentation ...

Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study.

PURPOSE: Whilst survival in paediatric critical care has improved, clinicians lack tools capable of ...

Digerati - A multipath parallel hybrid deep learning framework for the identification of mycobacterial PE/PPE proteins.

The genome of Mycobacterium tuberculosis contains a relatively high percentage (10%) of genes that a...

Functional Alignment-Auxiliary Generative Adversarial Network-Based Visual Stimuli Reconstruction via Multi-Subject fMRI.

Functional Magnetic Resonance Imaging (fMRI) provides more precise spatial and temporal information ...

Leveling Up: A Review of Machine Learning Models in the Cardiac ICU.

Machine learning has emerged as a significant tool to augment the medical decision-making process. S...

Pulmonary contusion: automated deep learning-based quantitative visualization.

PURPOSE: Rapid automated CT volumetry of pulmonary contusion may predict progression to Acute Respir...

FT-GAT: Graph neural network for predicting spontaneous breathing trial success in patients with mechanical ventilation.

BACKGROUND AND OBJECTIVES: Intensive care unit (ICU) physicians perform weaning procedures consideri...

A multi-modal deep neural network for multi-class liver cancer diagnosis.

Liver disease is a potentially asymptomatic clinical entity that may progress to patient death. This...

Geometric graph neural networks on multi-omics data to predict cancer survival outcomes.

The advance of sequencing technologies has enabled a thorough molecular characterization of the geno...

Soil carbon content prediction using multi-source data feature fusion of deep learning based on spectral and hyperspectral images.

Visible near-infrared reflectance spectroscopy (VNIR) and hyperspectral images (HSI) have their resp...

Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism.

Recently, several studies have proposed methods for measuring cuffless blood pressure (BP) using fin...

Deep learning-based prognostic model using non-enhanced cardiac cine MRI for outcome prediction in patients with heart failure.

OBJECTIVES: To evaluate the performance of a deep learning-based multi-source model for survival pre...

Understanding computational dialogue understanding.

In this paper, we first explain why human-like dialogue understanding is so difficult for artificial...

MM-GLCM-CNN: A multi-scale and multi-level based GLCM-CNN for polyp classification.

Distinguishing malignant from benign lesions has significant clinical impacts on both early detectio...

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