Critical Care

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

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Subcategories: Sepsis
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Effective Point Cloud Analysis Using Multi-Scale Features.

Fully exploring the correlation of local features and their spatial distribution in point clouds is ...

An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm.

Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use...

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, ris...

TSU-net: Two-stage multi-scale cascade and multi-field fusion U-net for right ventricular segmentation.

Accurate segmentation of the right ventricle from cardiac magnetic resonance images (MRI) is a criti...

Compressing deep graph convolution network with multi-staged knowledge distillation.

Given a trained deep graph convolution network (GCN), how can we effectively compress it into a comp...

Multi-Modal Residual Perceptron Network for Audio-Video Emotion Recognition.

Emotion recognition is an important research field for human-computer interaction. Audio-video emoti...

dSPIC: a deep SPECT image classification network for automated multi-disease, multi-lesion diagnosis.

BACKGROUND: Functional imaging especially the SPECT bone scintigraphy has been accepted as the effec...

Real-time prediction of intradialytic relative blood volume: a proof-of-concept for integrated cloud computing infrastructure.

BACKGROUND: Inadequate refilling from extravascular compartments during hemodialysis can lead to int...

A stochastic modeling approach for analyzing water resources systems.

Many uncertain factors exist in the water resource systems, leading to dynamic characteristics of th...

CNN-MoE Based Framework for Classification of Respiratory Anomalies and Lung Disease Detection.

This paper presents and explores a robust deep learning framework for auscultation analysis. This ai...

Multi-EPL: Accurate multi-source domain adaptation.

Given multiple source datasets with labels, how can we train a target model with no labeled data? Mu...

Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy.

BACKGROUND: In anti-vascular endothelial growth factor (anti-VEGF) therapy, an accurate estimation o...

Deep graph neural network-based prediction of acute suicidal ideation in young adults.

Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide pre...

Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning.

In two-thirds of intensive care unit (ICU) patients and 90% of surgical patients, arterial blood pre...

Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer's disease in a cross-sectional multi-cohort study.

Normative modelling is an emerging method for quantifying how individuals deviate from the healthy p...

Learning to Segment From Scribbles Using Multi-Scale Adversarial Attention Gates.

Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to obtain, ...

COVID-19 diagnosis and severity detection from CT-images using transfer learning and back propagation neural network.

BACKGROUND: COVID-19 diagnosis in symptomatic patients is an important factor for arranging the nece...

Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images.

Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances i...

MSF-Net: Multi-Scale Feature Learning Network for Classification of Surface Defects of Multifarious Sizes.

In the field of surface defect detection, the scale difference of product surface defects is often h...

Dialysis adequacy predictions using a machine learning method.

Dialysis adequacy is an important survival indicator in patients with chronic hemodialysis. However,...

A Deep Neural Network-Based Multi-Frequency Path Loss Prediction Model from 0.8 GHz to 70 GHz.

Large-scale fading models play an important role in estimating radio coverage, optimizing base stati...

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