Critical Care

Deep learning-based framework for motion-compensated image fusion in catheterization procedures.

OBJECTIVE: Augmenting X-ray (XR) fluoroscopy with 3D anatomic overlays is an essential technique to improve the guidance of the catheterization procedures. Unfortunately, cardiac and respiratory motion compromises the augmented fluoroscopy. Motion compensation methods can be applied to update the overlay of a static model with regard to respiratory and cardiac motion. We investigate the feasibilit...

A temporal dependency feature in lower dimension for lung sound signal classification.

Respiratory sounds are expressed as nonlinear and nonstationary signals, whose unpredictability makes it difficult to extract significant features for classification. Static cepstral coefficients such as Mel-frequency cepstral coefficients (MFCCs), have been used for classification of lung sound signals. However, they are modeled in high-dimensional hyperspectral space, and also lose temporal depe...

Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis?

OBJECTIVE: To analyze the critical alarms predictors of clinical deterioration/sepsis for clinical decision making in patients admitted to a reference...

Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods.

OBJECTIVE: Based on the respiratory disease big data platform in southern Xinjiang, we established a model that predicted and diagnosed chronic obstru...

Challenging molecular dogmas in human sepsis using mathematical reasoning.

Sepsis is defined as a dysregulated host-response to infection, across all ages and pathogens. What defines a dysregulated state remains intensively r...

Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset.

The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applicatio...

Multi-Output Selective Ensemble Identification of Nonlinear and Nonstationary Industrial Processes.

A key characteristic of biological systems is the ability to update the memory by learning new knowledge and removing out-of-date knowledge so that in...

Deep Learning Based Joint PET Image Reconstruction and Motion Estimation.

Respiratory motion is one of the main sources of motion artifacts in positron emission tomography (PET) imaging. The emission image and patient motion...

POTTER-ICU: An artificial intelligence smartphone-accessible tool to predict the need for intensive care after emergency surgery.

BACKGROUND: Delays in admitting high-risk emergency surgery patients to the intensive care unit result in worse outcomes and increased health care cos...

Task-State EEG Signal Classification for Spatial Cognitive Evaluation Based on Multiscale High-Density Convolutional Neural Network.

In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method for spatial cognitive ability assessment was prop...

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 due to high dimensionality, uncertainty in sampli...

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 crowd counting, which effectively uses contextual inf...

Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU.

OBJECTIVES: Unrecognized clinical deterioration during illness requiring hospitalization is associated with high risk of mortality and long-term morbi...

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 prognostication, ML has not been used to predict sepsi...

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 and implementation of triage guidelines in ICU for ...

Evolution and Neural Network Prediction of CO Emissions in Weaned Piglet Farms.

This paper aims to study the evolution of CO concentrations and emissions on a conventional farm with weaned piglets between 6.9 and 17.0 kg live weig...

3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility.

This paper proposes a time-series deep-learning 3D Kinect camera scheme to classify the respiratory phases with a lung tumor and predict the lung tumo...

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 patient electronic medical record (EMR) and physiol...

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, an alternative method to evaluate haemodynamic stab...

Think positive: An interpretable neural network for image recognition.

The COVID-19 pandemic is an ongoing pandemic and is placing additional burden on healthcare systems around the world. Timely and effectively detecting...