Critical Care

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

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Showing 1471-1491 of 7,427 articles
Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks.

OBJECTIVE: This study aims to design, validate and assess the accuracy a deep learning model capable...

Generalized latent multi-view clustering with tensorized bipartite graph.

Tensor-based multi-view spectral clustering algorithms use tensors to model the structure of multi-d...

Medical image segmentation network based on multi-scale frequency domain filter.

With the development of deep learning, medical image segmentation in computer-aided diagnosis has be...

Two-step interpretable modeling of ICU-AIs.

We present a novel methodology for integrating high resolution longitudinal data with the dynamic pr...

Machine learning in risk prediction of continuous renal replacement therapy after coronary artery bypass grafting surgery in patients.

OBJECTIVES: This study aimed to develop machine learning models for risk prediction of continuous re...

A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning.

PURPOSE: Radiotherapy planning incorporating functional lung images has the potential to reduce pulm...

Benchmarking machine learning-based real-time respiratory signal predictors in 4D SBRT.

BACKGROUND: Stereotactic body radiotherapy of thoracic and abdominal tumors has to account for respi...

Enhancing thermal comfort prediction in high-speed trains through machine learning and physiological signals integration.

Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible...

Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning.

BACKGROUND: Classifying breast cancer subtypes is crucial for clinical diagnosis and treatment. Howe...

Predicting intubation for intensive care units patients: A deep learning approach to improve patient management.

OBJECTIVE: For patients in the Intensive Care Unit (ICU), the timing of intubation has a significant...

3D multi-robot olfaction in naturally ventilated indoor environments: Locating a time-varying source at unknown heights.

Source localization is significant for mitigating indoor air pollution and safeguarding the well-bei...

Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images.

Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility f...

DL-EDOF: Novel Multi-Focus Image Data Set and Deep Learning-Based Approach for More Accurate and Specimen-Free Extended Depth of Focus.

Depth of focus (DOF) is defined as the axial range in which the specimen stage moves without losing ...

A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm.

We constructed an early prediction model for postoperative pulmonary complications after thoracoscop...

Integrated image and location analysis for wound classification: a deep learning approach.

The global burden of acute and chronic wounds presents a compelling case for enhancing wound classif...

Automatic multi-view pose estimation in focused cardiac ultrasound.

Focused cardiac ultrasound (FoCUS) is a valuable point-of-care method for evaluating cardiovascular ...

Robust Deep Neural Network for Learning in Noisy Multi-Label Food Images.

Deep networks can facilitate the monitoring of a balanced diet to help prevent various health proble...

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