Hospital-Based Medicine

Intensivists

Latest AI and machine learning research in intensivists for healthcare professionals.

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Showing 2164-2184 of 6,181 articles
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...

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,...

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...

An artificial neural network model for prediction of hypoxemia during sedation for gastrointestinal endoscopy.

OBJECTIVE: This study was designed to assess clinical predictors of hypoxemia and develop an artific...

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...

A dense multi-path decoder for tissue segmentation in histopathology images.

BACKGROUND AND OBJECTIVE: Segmenting different tissue components in histopathological images is of g...

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...

A multi-task convolutional deep neural network for variant calling in single molecule sequencing.

The accurate identification of DNA sequence variants is an important, but challenging task in genomi...

Classifying Biomedical Figures by Modality via Multi-Label Learning.

The figures found in biomedical literature are a vital part of biomedical research, education, and c...

Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks.

Acquiring images of the same anatomy with multiple different contrasts increases the diversity of di...

Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data.

BACKGROUND: Rapid antibiotic administration is known to improve sepsis outcomes, however early diagn...

Surface-Electromyography-Based Gesture Recognition by Multi-View Deep Learning.

Gesture recognition using sparse multichannel surface electromyography (sEMG) is a challenging probl...

An attention based deep learning model of clinical events in the intensive care unit.

This study trained long short-term memory (LSTM) recurrent neural networks (RNNs) incorporating an a...

Optimal adaptive control of drug dosing using integral reinforcement learning.

In this paper, a reinforcement learning (RL)-based optimal adaptive control approach is proposed for...

Potential EEG biomarkers of sedation doses in intensive care patients unveiled by using a machine learning approach.

OBJECTIVE: Sedation of neurocritically ill patients is one of the most challenging situation in ICUs...

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