Intensivists

Learning to predict in-hospital mortality risk in the intensive care unit with attention-based temporal convolution network.

BACKGROUND: Dynamic prediction of patient mortality risk in the ICU with time series data is limited...

Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting.

In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense cro...

Soft pneumatic actuators for mimicking multi-axial femoropopliteal artery mechanobiology.

Tissue biomanufacturing aims to produce lab-grown stem cell grafts and biomimetic drug testing platf...

Multi-attack and multi-classification intrusion detection for vehicle-mounted networks based on mosaic-coded convolutional neural network.

With the development of Internet of vehicles, the information exchange between vehicles and the outs...

Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning.

BACKGROUND: There is progress to be made in building artificially intelligent systems to detect abno...

COVID Detection From Chest X-Ray Images Using Multi-Scale Attention.

Deep learning based methods have shown great promise in achieving accurate automatic detection of Co...

AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy.

Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in...

Learning Multi-Scale Heterogeneous Representations and Global Topology for Drug-Target Interaction Prediction.

Identification of interactions between drugs and target proteins plays a critical role not only in d...

Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients.

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development a...

Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach.

BACKGROUND: Although machine learning (ML) algorithms have been applied to point-of-care sepsis prog...

MC-GCN: A Multi-Scale Contrastive Graph Convolutional Network for Unconstrained Face Recognition With Image Sets.

In this paper, a Multi-scale Contrastive Graph Convolutional Network (MC-GCN) method is proposed for...

Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset.

The objective of this work is to develop a fusion artificial intelligence (AI) model that combines p...

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU.

BACKGROUND: Although serum lactate levels are widely accepted markers of haemodynamic instability, a...

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning.

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of C...

Deep Learning Methods for Multi-Channel EEG-Based Emotion Recognition.

Currently, Fourier-based, wavelet-based, and Hilbert-based time-frequency techniques have generated ...

Multi-Modal Classification for Human Breast Cancer Prognosis Prediction: Proposal of Deep-Learning Based Stacked Ensemble Model.

Breast Cancer is a highly aggressive type of cancer generally formed in the cells of the breast. Des...

Multi-Scale Attention Convolutional Network for Masson Stained Bile Duct Segmentation from Liver Pathology Images.

In clinical practice, the Ishak Score system would be adopted to perform the evaluation of the gradi...

Sepsis labels defined by claims-based methods are ill-suited for training machine learning algorithms.

PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods.

Bioinformatic annotation of protein function is essential but extremely sophisticated, which asks fo...

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients.

In today's scenario, sepsis is impacting millions of patients in the intensive care unit due to the ...

Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data.

Rolling element bearing faults significantly contribute to overall machine failures, which demand di...