Hospital-Based Medicine

Intensivists

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

6,177 articles
Stay Ahead - Weekly Intensivists research updates
Subscribe
Browse Categories
Showing 1996-2016 of 6,177 articles
Multi-task multi-modal learning for joint diagnosis and prognosis of human cancers.

With the tremendous development of artificial intelligence, many machine learning algorithms have be...

Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept.

Sepsis is the primary cause of burn-related mortality and morbidity. Traditional indicators of sepsi...

Multi-site household waste generation forecasting using a deep learning approach.

Forecasting household waste generation using traditional methods is particularly challenging due to ...

Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?

Gait is a characteristic that has been utilized for identifying individuals. As human gait informati...

An attention-based multi-task model for named entity recognition and intent analysis of Chinese online medical questions.

In this paper, we propose an attention-based multi-task neural network model for text classification...

Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results.

Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging (dMRI) data are...

Automated design and optimization of multitarget schizophrenia drug candidates by deep learning.

Complex neuropsychiatric diseases such as schizophrenia require drugs that can target multiple G pro...

Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines.

In this work, a novel data-driven fault diagnostic framework is developed by using hybrid multi-mode...

Realistic simulation of virtual multi-scale, multi-modal patient trajectories using Bayesian networks and sparse auto-encoders.

Translational research of many disease areas requires a longitudinal understanding of disease develo...

MH-MetroNet-A Multi-Head CNN for Passenger-Crowd Attendance Estimation.

Knowing an accurate passengers attendance estimation on each metro car contributes to the safely coo...

Benchmarking machine learning models on multi-centre eICU critical care dataset.

Progress of machine learning in critical care has been difficult to track, in part due to absence of...

Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning.

Continual learning, a widespread ability in people and animals, aims to learn and acquire new knowle...

Towards Robust and Accurate Detection of Abnormalities in Musculoskeletal Radiographs with a Multi-Network Model.

This study proposes a novel multi-network architecture consisting of a multi-scale convolution neura...

Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning.

Drug-drug interactions (DDIs) are crucial for public health and patient safety, which has aroused wi...

CAST: A multi-scale convolutional neural network based automated hippocampal subfield segmentation toolbox.

In this study, we developed a multi-scale Convolutional neural network based Automated hippocampal s...

Multi-channel lung sound classification with convolutional recurrent neural networks.

In this paper, we present an approach for multi-channel lung sound classification, exploiting spectr...

The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit.

BACKGROUND: Severe sepsis and septic shock are still the leading causes of death in Intensive Care U...

Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data.

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in sel...

Accurate and efficient sequential ensemble learning for highly imbalanced multi-class data.

Multi-class classification for highly imbalanced data is a challenging task in which multiple issues...

Browse Categories