Critical Care

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

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RNA-Protein Binding Sites Prediction via Multi Scale Convolutional Gated Recurrent Unit Networks.

RNA-Protein binding plays important roles in the field of gene expression. With the development of h...

Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests.

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on e...

An Equation Based on Fuzzy Mathematics to Assess the Timing of Haemodialysis Initiation.

In order to develop an equation that integrates multiple clinical factors including signs and sympto...

Towards real-time respiratory motion prediction based on long short-term memory neural networks.

Radiation therapy of thoracic and abdominal tumors requires incorporating the respiratory motion int...

Capsule Network Based Modeling of Multi-omics Data for Discovery of Breast Cancer-Related Genes.

Breast cancer is one of the most common cancers all over the world, which bring about more than 450,...

Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units.

BACKGROUND: Reinforcement learning (RL) provides a promising technique to solve complex sequential d...

Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU.

Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs...

Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems.

This article examines the history of the telemedicine intensive care unit (tele-ICU), the current st...

Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs.

Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological pr...

Machine learning for patient risk stratification for acute respiratory distress syndrome.

BACKGROUND: Existing prediction models for acute respiratory distress syndrome (ARDS) require manual...

Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features.

Accurate prognosis of patients with cancer is important for the stratification of patients, the opti...

Multi-Modal Haptic Feedback for Grip Force Reduction in Robotic Surgery.

Minimally invasive robotic surgery allows for many advantages over traditional surgical procedures, ...

A multi-scale data fusion framework for bone age assessment with convolutional neural networks.

Bone age assessment (BAA) has various clinical applications such as diagnosis of endocrine disorders...

Scalable Digital Neuromorphic Architecture for Large-Scale Biophysically Meaningful Neural Network With Multi-Compartment Neurons.

Multicompartment emulation is an essential step to enhance the biological realism of neuromorphic sy...

Expert-level classification of ventilatory thresholds from cardiopulmonary exercising test data with recurrent neural networks.

First and second ventilatory thresholds (VT and VT) represent the boundaries of the moderate-heavy a...

Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection.

Lung cancer is a global and dangerous disease, and its early detection is crucial for reducing the r...

Discriminative multi-source adaptation multi-feature co-regression for visual classification.

Learning an effective visual classifier from few labeled samples is a challenging problem, which has...

Technical Note: Deriving ventilation imaging from 4DCT by deep convolutional neural network.

PURPOSE: Ventilation images can be derived from four-dimensional computed tomography (4DCT) by analy...

Emotional arousal amplifies competitions across goal-relevant representation: A neurocomputational framework.

Emotional arousal often facilitates memory for some aspects of an event while impairing memory for o...

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