Critical Care

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

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Subcategories: Sepsis
Showing 820-840 of 7,422 articles
An EEG-based emotion recognition method by fusing multi-frequency-spatial features under multi-frequency bands.

BACKGROUND: Recognition of emotion changes is of great significance to a person's physical and menta...

MSRMMP: Multi-scale residual module and multi-layer pseudo-supervision for weakly supervised segmentation of histopathological images.

Accurate semantic segmentation of histopathological images plays a crucial role in accurate cancer d...

External validation of AI-based scoring systems in the ICU: a systematic review and meta-analysis.

BACKGROUND: Machine learning (ML) is increasingly used to predict clinical deterioration in intensiv...

Interpretable machine learning for predicting sepsis risk in emergency triage patients.

The study aimed to develop and validate a sepsis prediction model using structured electronic medica...

Retrospective analysis of amantadine response and predictive factors in intensive care unit patients with non-traumatic disorders of consciousness.

BACKGROUND: Disorders of consciousness (DoC) in non-traumatic ICU-patients are often treated with am...

Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients.

BACKGROUND: Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing ...

Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring.

Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and...

Multi-level feature fusion networks for smoke recognition in remote sensing imagery.

Smoke is a critical indicator of forest fires, often detectable before flames ignite. Accurate smoke...

A discriminative multi-modal adaptation neural network model for video action recognition.

Research on video-based understanding and learning has attracted widespread interest and has been ad...

Synergistic learning with multi-task DeepONet for efficient PDE problem solving.

Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information...

Multi-view clustering based on feature selection and semi-non-negative anchor graph factorization.

Multi-view clustering has garnered significant attention due to its capacity to utilize information ...

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

BACKGROUND: Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be prev...

Evaluation of an enhanced ResNet-18 classification model for rapid On-site diagnosis in respiratory cytology.

OBJECTIVE: Rapid on-site evaluation (ROSE) of respiratory cytology specimens is a critical technique...

Temporal multi-modal knowledge graph generation for link prediction.

Temporal Multi-Modal Knowledge Graphs (TMMKGs) can be regarded as a synthesis of Temporal Knowledge ...

PFSH-Net: Parallel frequency-spatial hybrid network for segmentation of kidney stones in pre-contrast computed tomography images of dogs.

Kidney stone is a common urological disease in dogs and can lead to serious complications such as py...

Multi-Modal Federated Learning for Cancer Staging Over Non-IID Datasets With Unbalanced Modalities.

The use of machine learning (ML) for cancer staging through medical image analysis has gained substa...

Generative Adversarial Network With Robust Discriminator Through Multi-Task Learning for Low-Dose CT Denoising.

Reducing the dose of radiation in computed tomography (CT) is vital to decreasing secondary cancer r...

Bridging MRI Cross-Modality Synthesis and Multi-Contrast Super-Resolution by Fine-Grained Difference Learning.

In multi-modal magnetic resonance imaging (MRI), the tasks of imputing or reconstructing the target ...

OTMorph: Unsupervised Multi-Domain Abdominal Medical Image Registration Using Neural Optimal Transport.

Deformable image registration is one of the essential processes in analyzing medical images. In part...

Multi-Modal Diagnosis of Alzheimer's Disease Using Interpretable Graph Convolutional Networks.

The interconnection between brain regions in neurological disease encodes vital information for the ...

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