Critical Care

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

7,422 articles
Stay Ahead - Weekly Critical Care research updates
Subscribe
Browse Specialties
Subcategories: Sepsis
Showing 631-651 of 7,422 articles
Detection of human activities using multi-layer convolutional neural network.

Human Activity Recognition (HAR) plays a critical role in fields such as healthcare, sports, and hum...

Machine Learning-Based Mortality Prediction for Acute Gastrointestinal Bleeding Patients Admitted to Intensive Care Unit.

OBJECTIVE: The study aimed to develop machine learning (ML) models to predict the mortality of patie...

Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.

PURPOSE: Respiratory motion during PET acquisition may produce lesion blurring. Ultra-fast 20-second...

Multi-axis transformer based U-Net with class balanced ensemble model for lung disease classification using X-ray images.

Chest X-rays are an essential diagnostic tool for identifying chest disorders because of its high s...

Back Propagation Artificial Neural Network Enhanced Accuracy of Multi-Mode Sensors.

The detection of small molecules is critical in many fields, but traditional electrochemical detecti...

Imbalanced Power Spectral Generation for Respiratory Rate and Uncertainty Estimations Based on Photoplethysmography Signal.

Respiratory rate (RR) changes in the elderly can indicate serious diseases. Thus, accurate estimatio...

Prioritisation of functional needs for ICU intelligent robots in China: a consensus study based on the national survey and nominal group technique.

OBJECTIVE: This study aims to define the prioritisation of the needs for an intelligent robot's func...

Predicting Agitation-Sedation Levels in Intensive Care Unit Patients: Development of an Ensemble Model.

BACKGROUND: Agitation and sedation management is critical in intensive care as it affects patient sa...

Multi-omics analyses and machine learning prediction of oviductal responses in the presence of gametes and embryos.

The oviduct is the site of fertilization and preimplantation embryo development in mammals. Evidence...

Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning.

BACKGROUND: Sepsis is a life-threatening disease causing millions of deaths every year. It has been ...

Continuous non-contact monitoring of neonatal activity.

PURPOSE: Neonatal activity is an important physiological parameter in the neonatal intensive care un...

RAE-Net: a multi-modal neural network based on feature fusion and evidential deep learning algorithm in predicting breast cancer subtypes on DCE-MRI.

Accurate identification of molecular subtypes in breast cancer is critical for personalized treatmen...

MSTNet: Multi-scale spatial-aware transformer with multi-instance learning for diabetic retinopathy classification.

Diabetic retinopathy (DR), the leading cause of vision loss among diabetic adults worldwide, undersc...

Optimizing depression detection in clinical doctor-patient interviews using a multi-instance learning framework.

In recent years, the number of people suffering from depression has gradually increased, and early d...

GeM: Gaussian embeddings with Multi-hop graph transfer for next POI recommendation.

Next Point-of-Interest (POI) recommendation is crucial in location-based applications, analyzing use...

MERIT: Multi-view evidential learning for reliable and interpretable liver fibrosis staging.

Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical prac...

Dissolved organic carbon estimation in lakes: Improving machine learning with data augmentation on fusion of multi-sensor remote sensing observations.

This paper presents a novel approach for estimating Dissolved Organic Carbon (DOC) concentrations in...

Visit-to-visit blood pressure variability and clinical outcomes in peritoneal dialysis - based on machine learning algorithms.

This study aims to investigate the association between visit-to-visit blood pressure variability (VV...

Missing-modality enabled multi-modal fusion architecture for medical data.

BACKGROUND: Fusion of multi-modal data can improve the performance of deep learning models. However,...

Browse Specialties