Critical Care

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

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Subcategories: Sepsis
Showing 589-609 of 7,417 articles
Lung cancer detection with machine learning classifiers with multi-attribute decision-making system and deep learning model.

Diseases of the airways and the other parts of the lung cause chronic respiratory diseases. The majo...

Development and validation of machine learning models for predicting extubation failure in patients undergoing cardiac surgery: a retrospective study.

Patients with multiple comorbidities and those undergoing complex cardiac surgery may experience ext...

Neuronal and therapeutic perspectives on empathic pain: A rational insight.

Empathy is the capacity to experience and understand the feelings of others, thereby playing a key r...

Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand.

This study aims to construct a prediction model for the demand for medical and daily care services o...

Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets.

Sepsis represents a significant global health challenge, necessitating early detection and effective...

An intelligent multi-attribute decision-making system for clinical assessment of spinal cord disorder using fuzzy hypersoft rough approximations.

The data for diagnosing spinal cord disorder (SCD) are complex and often confusing, making it diffic...

Development and validation of an interpretable machine learning model for predicting in-hospital mortality for ischemic stroke patients in ICU.

BACKGROUND: Timely and accurate outcome prediction is essential for clinical decision-making for isc...

Harnessing machine learning for predicting successful weaning from mechanical ventilation: A systematic review.

BACKGROUND: Machine learning (ML) models represent advanced computational approaches with increasing...

Integrated fusion approach for multi-class heart disease classification through ECG and PCG signals with deep hybrid neural networks.

Detection and classification of cardiovascular diseases are crucial for early diagnosis and predicti...

Automatic cerebral microbleeds detection from MR images via multi-channel and multi-scale CNNs.

BACKGROUND: Computer-aided detection (CAD) systems have been widely used to assist medical professio...

Dynamic Prediction and Intervention of Serum Sodium in Patients with Stroke Based on Attention Mechanism Model.

Abnormal serum sodium levels are a common and severe complication in stroke patients, significantly ...

Deep reinforcement learning for multi-targets propofol dosing.

The administration of propofol for sedation or general anesthesia presents challenges due to the com...

Leveraging Artificial Intelligence to Reduce Neuroscience ICU Length of Stay.

GOAL: Efficient patient flow is critical at Tampa General Hospital (TGH), a large academic tertiary ...

A multi-stage fusion deep learning framework merging local patterns with attention-driven contextual dependencies for cancer detection.

Cancer is a severe threat to public health. Early diagnosis of disease is critical, but the lack of ...

Development and Validation of an Electronic Health Record-Based, Pediatric Acute Respiratory Distress Syndrome Subphenotype Classifier Model.

OBJECTIVE: To determine if hyperinflammatory and hypoinflammatory pediatric acute respiratory distre...

UGS-M3F: unified gated swin transformer with multi-feature fully fusion for retinal blood vessel segmentation.

Automated segmentation of retinal blood vessels in fundus images plays a key role in providing ophth...

Hyperspectral discrimination of vegetable crops grown under organic and conventional cultivation practices: a machine learning approach.

A verifiable and regional level method for mapping crops cultivated under organic practices holds si...

OMS-CNN: Optimized Multi-Scale CNN for Lung Nodule Detection Based on Faster R-CNN.

The global increase in lung cancer cases, often marked by pulmonary nodules, underscores the critica...

Drug Repositioning via Multi-View Representation Learning With Heterogeneous Graph Neural Network.

Exploring simple and efficient computational methods for drug repositioning has emerged as a popular...

A damage identification method for aviation structure integrating Lamb wave and deep learning with multi-dimensional feature fusion.

With the development of aerospace industry, a more suitable structural health monitoring (SHM) metho...

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