Intensivists

FT-GAT: Graph neural network for predicting spontaneous breathing trial success in patients with mechanical ventilation.

BACKGROUND AND OBJECTIVES: Intensive care unit (ICU) physicians perform weaning procedures consideri...

Soil carbon content prediction using multi-source data feature fusion of deep learning based on spectral and hyperspectral images.

Visible near-infrared reflectance spectroscopy (VNIR) and hyperspectral images (HSI) have their resp...

Deep learning-based prognostic model using non-enhanced cardiac cine MRI for outcome prediction in patients with heart failure.

OBJECTIVES: To evaluate the performance of a deep learning-based multi-source model for survival pre...

Understanding computational dialogue understanding.

In this paper, we first explain why human-like dialogue understanding is so difficult for artificial...

Strategy to implement a convolutional neural network based ideal model observer via transfer learning for multi-slice simulated breast CT images.

In this work, we propose a convolutional neural network (CNN)-based multi-slice ideal model observer...

The Evolution and Future of Intensive Care Management in the Era of Telecritical Care and Artificial Intelligence.

Critical care practice has been embodied in the healthcare system since the institutionalization of ...

Identifying acute kidney injury subphenotypes using an outcome-driven deep-learning approach.

OBJECTIVE: Acute kidney injury (AKI), a common condition on the intensive-care unit (ICU), is charac...

Human Collective Intelligence Inspired Multi-View Representation Learning - Enabling View Communication by Simulating Human Communication Mechanism.

In real-world applications, we often encounter multi-view learning tasks where we need to learn from...

A dosing strategy model of deep deterministic policy gradient algorithm for sepsis patients.

BACKGROUND: A growing body of research suggests that the use of computerized decision support system...

Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model.

BACKGROUND: Identifying patterns within ICU medication regimens may help artificial intelligence alg...

Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and scalable deep learning-based solution.

Federated learning (FL) is a privacy preserving approach to learning that overcome issues related to...

Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data: Advantages and Challenges.

The rapid adoption of electronic health record (EHR) systems in US hospitals from 2008 to 2014 produ...

Bringing the Promise of Artificial Intelligence to Critical Care: What the Experience With Sepsis Analytics Can Teach Us.

Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit.

INTRODUCTION: Digital twins, a form of artificial intelligence, are virtual representations of the p...

Multi-event survival analysis through dynamic multi-modal learning for ICU mortality prediction.

BACKGROUND AND OBJECTIVE: Survival analysis is widely applied for assessing the expected duration of...

Multi-View Graph Neural Architecture Search for Biomedical Entity and Relation Extraction.

Recently, graph neural architecture search (GNAS) frameworks have been successfully used to automati...

A hybrid model- and deep learning-based framework for functional lung image synthesis from multi-inflation CT and hyperpolarized gas MRI.

BACKGROUND: Hyperpolarized gas MRI is a functional lung imaging modality capable of visualizing regi...

Multi-modal body part segmentation of infants using deep learning.

BACKGROUND: Monitoring the body temperature of premature infants is vital, as it allows optimal temp...

Improvement of multi-task learning by data enrichment: application for drug discovery.

Multi-task learning in deep neural networks has become a topic of growing importance in many researc...

A multi-task and multi-channel convolutional neural network for semi-supervised neonatal artefact detection.

. Automated artefact detection in the neonatal electroencephalogram (EEG) is crucial for reliable au...

A deep learning system for heart failure mortality prediction.

Heart failure (HF) is the final stage of the various heart diseases developing. The mortality rates ...