Critical Care

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

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Showing 2710-2730 of 7,452 articles
Multi-Scale Self-Guided Attention for Medical Image Segmentation.

Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation,...

On the regularization of feature fusion and mapping for fast MR multi-contrast imaging via iterative networks.

Multi-contrast (MC) Magnetic Resonance Imaging (MRI) of the same patient usually requires long scann...

The Combined Effects of Sports Smart Bracelet and Multi-Component Exercise Program on Exercise Motivation among the Elderly in Macau.

Faced with the serious problem of an aging population, exercise is one of the most effective ways t...

Caffeine mouth rinse enhances performance, fatigue tolerance and reduces muscle activity during moderate-intensity cycling.

We investigated the effects of caffeine mouth rinse on endurance performance, muscle recruitment (i....

Multi-objective optimization methods in novel drug design.

: In multi-objective drug design, optimization gains importance, being upgraded to a discipline that...

Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit.

Traditional Machine Learning (ML) models have had limited success in predicting Coronoavirus-19 (COV...

Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.

BACKGROUND: Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemi...

Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods.

Transportation safety is highly correlated with driving behavior, especially human error playing a k...

Non-Rigid Respiratory Motion Estimation of Whole-Heart Coronary MR Images Using Unsupervised Deep Learning.

Non-rigid motion-corrected reconstruction has been proposed to account for the complex motion of the...

Generative Restricted Kernel Machines: A framework for multi-view generation and disentangled feature learning.

This paper introduces a novel framework for generative models based on Restricted Kernel Machines (R...

Exploration of swimming performance for a biomimetic multi-joint robotic fish with a compliant passive joint.

In this paper, a novel compliant joint with two identical torsion springs is proposed for a biomimet...

Deep learning-based real-time volumetric imaging for lung stereotactic body radiation therapy: a proof of concept study.

Due to the inter- and intra- variation of respiratory motion, it is highly desired to provide real-t...

Development and Prospective Validation of a Deep Learning Algorithm for Predicting Need for Mechanical Ventilation.

BACKGROUND: Objective and early identification of hospitalized patients, and particularly those with...

A deep learning backcasting approach to the electrolyte, metabolite, and acid-base parameters that predict risk in ICU patients.

BACKGROUND: A powerful risk model allows clinicians, at the bedside, to ensure the early identificat...

Fusion Learning on Multiple-Tag RFID Measurements for Respiratory Rate Monitoring.

Future advances in the medical Internet of Things (IoT) will require sensors that are unobtrusive an...

Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques.

In recent years, the widespread deployment of the Internet of Things (IoT) applications has contribu...

A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization.

Adversarial attack techniques in deep learning have been studied extensively due to its stealthiness...

Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach.

BACKGROUND: The study aimed to introduce a machine learning model that predicts in-hospital mortalit...

Early initiation of PD therapy in elderly patients is associated with increased risk of death.

BACKGROUND: The effect of early initiation of dialysis on outcomes of patients with end-stage renal ...

Artificial intelligence in the diagnosis of pediatric allergic diseases.

Artificial intelligence (AI) is a field of data science pertaining to advanced computing machines ca...

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