Critical Care

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

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Showing 3634-3654 of 7,482 articles
Exploring the Latent Information in Spatial Transcriptomics Data via Multi-View Graph Convolutional Network Based on Implicit Contrastive Learning.

Latest developments in spatial transcriptomics enable thoroughly profiling of gene expression while ...

MEF-Net: Multi-scale and edge feature fusion network for intracranial hemorrhage segmentation in CT images.

Intracranial Hemorrhage (ICH) refers to cerebral bleeding resulting from ruptured blood vessels with...

Multi-layered data framework for enhancing postoperative outcomes and anaesthesia management through natural language processing.

Anaesthesia management is a critical aspect of perioperative care, directly influencing postoperativ...

Explainable deep stacking ensemble model for accurate and transparent brain tumor diagnosis.

Early detection of brain tumors in MRI images is vital for improving treatment results. However, dee...

Automatic cough detection via a multi-sensor smart garment using machine learning.

Coughing behavior is associated with conditions such as sleep apnea, asthma, and chronic obstructive...

Radiation oncology patients' perceptions of artificial intelligence and machine learning in cancer care: A multi-centre cross-sectional study.

AIM: The use of artificial intelligence (AI) and machine learning (ML) is increasingly widespread in...

A multi-scale convolutional LSTM-dense network for robust cardiac arrhythmia classification from ECG signals.

Cardiac arrhythmias are irregular heart rhythms that, if undetected, can lead to severe cardiovascul...

Advancing the frontier of artificial intelligence on emerging technologies to redefine cancer diagnosis and care.

BACKGROUND: Artificial Intelligence (AI) is capable of revolutionizing cancer therapy and advancing ...

Reinforcement learning using neural networks in estimating an optimal dynamic treatment regime in patients with sepsis.

OBJECTIVE: Early fluid resuscitation is crucial in the treatment of sepsis, yet the optimal dosage r...

multiPI-TransBTS: A multi-path learning framework for brain tumor image segmentation based on multi-physical information.

Brain Tumor Segmentation (BraTS) plays a critical role in clinical diagnosis, treatment planning, an...

Transforming pulmonary health care: the role of artificial intelligence in diagnosis and treatment.

INTRODUCTION: Respiratory diseases like pneumonia, asthma, and COPD are major global health concerns...

Development and External Validation of a Detection Model to Retrospectively Identify Patients With Acute Respiratory Distress Syndrome.

OBJECTIVE: The aim of this study was to develop and externally validate a machine-learning model tha...

Fusion of multi-scale feature extraction and adaptive multi-channel graph neural network for 12-lead ECG classification.

BACKGROUND AND OBJECTIVE: The 12-lead electrocardiography (ECG) is a widely used diagnostic method i...

Self-supervised multi-modality learning for multi-label skin lesion classification.

BACKGROUND: The clinical diagnosis of skin lesions involves the analysis of dermoscopic and clinical...

Utilizing semantically enhanced self-supervised graph convolution and multi-head attention fusion for herb recommendation.

Traditional Chinese herbal medicine has long been recognized as an effective natural therapy. Recent...

Text mining for case report articles on "peritoneal dialysis" from PubMed database.

INTRODUCTION: The number of published medical articles on peritoneal dialysis (PD) has been increasi...

Cross- and Intra-Image Prototypical Learning for Multi-Label Disease Diagnosis and Interpretation.

Recent advances in prototypical learning have shown remarkable potential to provide useful decision ...

Hierarchically Optimized Multiple Instance Learning With Multi-Magnification Pathological Images for Cerebral Tumor Diagnosis.

Accurate diagnosis of cerebral tumors is crucial for effective clinical therapeutics and prognosis. ...

scDMSC: Deep Multi-View Subspace Clustering for Single-Cell Multi-Omics Data.

Single-cell multi-omics sequencing technology comprehensively considers various molecular features t...

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