Critical Care

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

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MultiV_Nm: a prediction method for 2'-O-methylation sites based on multi-view features.

As a crucial class of chemical modifications, 2'-O-methylation modification (abbreviated as Nm) is w...

Novel machine learning models for the prediction of acute respiratory distress syndrome after liver transplantation.

Early prediction of acute respiratory distress syndrome (ARDS) after liver transplantation (LT) faci...

Discovery of multi-metal-layered double hydroxides for decontamination of iodate by machine learning-assisted experiments.

The development of novel materials for radioactive iodate adsorption is critical for nuclear waste m...

Development and validation of a machine learning model for real-time prediction of invasive mechanical ventilation weaning readiness.

PURPOSE: To develop and validate a bedside machine learning (ML) decision support tool for predictio...

A multi-object detection method for building fire warnings through artificial intelligence generated content.

Timely fire warnings are crucial for minimizing casualties during building fires. In this paper, a m...

Research on prediction method of well logging reservoir parameters based on Multi-TransFKAN model.

Accurate prediction of reservoir parameters is crucial for enhancing oil exploration efficiency and ...

MAVSD: A Multi-Angle View Segmentation Dataset for Detection of Solidago Canadensis L.

Recent advancements in computer vision and deep learning have advanced automated vegetation monitori...

Deep ensemble framework with Bayesian optimization for multi-lesion recognition in capsule endoscopy images.

In order to address the challenges posed by the large number of images acquired during wireless caps...

Artificial intelligence in the management of patient-ventilator asynchronies: A scoping review.

BACKGROUND: Patient-ventilator asynchronies (PVAs) are frequent complications in mechanically ventil...

Multi-Channel Disentangled Graph Neural Networks with Different Types of Self-constraints.

Graph Neural Network (GNN) is a popular semi-supervised graph representation learning method, whose ...

Unveiling sources of organophosphate esters in marine environments utilizing multi-factor multi-modal high-dimensional clustering algorithm.

In marine environments, the sources of organophosphate esters (OPEs), particularly emerging OPEs (eO...

A demand-centered scheduling framework for shared supercomputing resources: modeling, metrics, and case insights.

The exponential growth of artificial intelligence and data-intensive applications has led to a signi...

Apnea detection using wrist actigraphy in patients with heterogeneous sleep disorders.

Obstructive sleep apnea (OSA) and related hypoxia are well-established cardiovascular and neurocogni...

NSSI-Net: A Multi-Concept GAN for Non-Suicidal Self-Injury Detection Using High-Dimensional EEG in a Semi-Supervised Framework.

Non-suicidal self-injury (NSSI) is a serious threat to the physical and mental health of adolescents...

AlphaGrad: Normalized Gradient Descent for Adaptive Multi-loss Functions in EEG-based Motor Imagery Classification.

In this study, we propose AlphaGrad, a novel adaptive loss blending strategy for optimizing multi-ta...

EFCRFNet: A novel multi-scale framework for salient object detection.

Salient Object Detection (SOD) is a fundamental task in computer vision, aiming to identify prominen...

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