Critical Care

Development of a Machine Learning Model Predicting an ICU Admission for Patients with Elective Surgery and Its Prospective Validation in Clinical Practice.

Frequent utilization of the Intensive Care Unit (ICU) is associated with higher costs and decreased ...

Learning Portuguese Clinical Word Embeddings: A Multi-Specialty and Multi-Institutional Corpus of Clinical Narratives Supporting a Downstream Biomedical Task.

In this paper, we trained a set of Portuguese clinical word embedding models of different granularit...

[Research progress of the application of AI in the diagnosis and treatment of respiratory disease].

Prospect of using deep learning for predicting differentiation of myeloid progenitor cells after sepsis.

web-rMKL: a web server for dimensionality reduction and sample clustering of multi-view data based on unsupervised multiple kernel learning.

More and more affordable high-throughput techniques for measuring molecular features of biomedical s...

Cascaded multi-scale convolutional encoder-decoders for breast mass segmentation in high-resolution mammograms.

This paper addresses breast mass segmentation from high-resolution mammograms. To cope with strong c...

Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning.

Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specifi...

RespNet: A deep learning model for extraction of respiration from photoplethysmogram.

Respiratory ailments afflict a wide range of people and manifests itself through conditions like ast...

Reckoning respiratory signals to affectively decipher mental state.

Recognizing mental states from physiological signal is a concern not only for medical diagnostics, b...

A Multi-channel Deep Learning Approach for Segmentation of the Left Ventricular Endocardium from Cardiac Images.

Cardiac segmentation is the first most important step in assessing cardiac diseases. However, it sti...

A Comparison of SVM and CNN-LSTM Based Approach for Detecting Smoke Inhalations from Respiratory signal.

Wearable sensors have successfully been used in recent studies to monitor cigarette smoking events a...

Myocardial Infarction Detection Based on Multi-lead Ensemble Neural Network.

Automatic myocardial infarction (MI) detection using an electrocardiogram (ECG) is of great signific...

Novel Automatic Epilepsy Detection Method Multi-weight Transition Network.

The automatic diagnosis of epilepsy using Electroencephalogram (EEG) signals had always been an impo...

Multi-Modal Acute Stress Recognition Using Off-the-Shelf Wearable Devices.

Monitoring stress and, in general, emotions has attracted a lot of attention over the past few decad...

A Reliable Multi-classifier Multi-objective Model for Predicting Recurrence in Triple Negative Breast Cancer.

Recurrence is a significant prognostic factor in patients with triple negative breast cancer, and th...

Sleep Apnea Severity Estimation from Respiratory Related Movements Using Deep Learning.

Sleep apnea is a common chronic respiratory disorder which occurs due to the repetitive complete or ...

OpenArm 2.0: Automated Segmentation of 3D Tissue Structures for Multi-Subject Study of Muscle Deformation Dynamics.

We present a novel neural-network-based pipeline for segmentation of 3D muscle and bone structures f...

Automatic Detection of Focal Liver Lesions in Multi-phase CT Images Using A Multi-channel & Multi-scale CNN.

There are multiple types of tumors occurring in the liver, each of which have a different visual app...

Comparing Machine Learning Algorithms for Predicting Acute Kidney Injury.

Prior studies have used vital signs and laboratory measurements with conventional modeling technique...

Inverting the Turing Test - Machine Learning to Detect Cognition in the ICU.

[Research on algorithms for identifying the severity of acute respiratory distress syndrome patients based on noninvasive parameters].

Acute respiratory distress syndrome (ARDS) is a serious threat to human life and health disease, wit...