Critical Care

Latest AI and machine learning research in critical care for healthcare professionals.

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Subcategories: Sepsis
Showing 2815-2835 of 7,452 articles
Utilizing imbalanced electronic health records to predict acute kidney injury by ensemble learning and time series model.

BACKGROUND: Acute Kidney Injury (AKI) is a shared complication among Intensive Care Unit (ICU), mark...

Application of machine learning to the prediction of postoperative sepsis after appendectomy.

BACKGROUND: We applied various machine learning algorithms to a large national dataset to model the ...

Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients.

INTRODUCTION: The Medication Regimen Complexity -Intensive Care Unit (MRC-ICU) is the first tool for...

A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants.

Survivors following very premature birth (i.e., ≤ 32 weeks gestational age) remain at high risk for ...

Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis.

BACKGROUND: Breast cancer is the most prevalent and among the most deadly cancers in females. Patien...

Toward a hemorrhagic trauma severity score: fusing five physiological biomarkers.

BACKGROUND: To introduce the Hemorrhage Intensive Severity and Survivability (HISS) score, based on ...

Adaptive respiratory signal prediction using dual multi-layer perceptron neural networks.

To improve the prediction accuracy of respiratory signals by adapting the multi-layer perceptron neu...

Supervised classification techniques for prediction of mortality in adult patients with sepsis.

BACKGROUND: Sepsis mortality is still unacceptably high and an appropriate prognostic tool may incre...

Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals.

Infants are at risk for potentially life-threatening postoperative apnea (POA). We developed an Auto...

A gene prioritization method based on a swine multi-omics knowledgebase and a deep learning model.

The analyses of multi-omics data have revealed candidate genes for objective traits. However, they a...

Data-driven ICU management: Using Big Data and algorithms to improve outcomes.

The digitalization of the Intensive Care Unit (ICU) led to an increasing amount of clinical data bei...

Incomplete multi-view gene clustering with data regeneration using Shape Boltzmann Machine.

Deciphering patterns in the structural and functional anatomy of genes can prove to be very helpful ...

Deep learning with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping.

Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced myocardial tiss...

Wearable multi-sensing double-chain thermoelectric generator.

Wearable electronics play a crucial role in advancing the rapid development of artificial intelligen...

The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model.

As is known, cerebral stroke has become one of the main diseases endangering people's health; ischae...

Stochastic DCA for minimizing a large sum of DC functions with application to multi-class logistic regression.

We consider the large sum of DC (Difference of Convex) functions minimization problem which appear i...

An Integrated Multi-Sensor Network for Adaptive Grasping of Fragile Fruits: Design and Feasibility Tests.

Secure grasping of fragile fruits and other agricultural products without potential slip and damage ...

Multi-dimensional predictions of psychotic symptoms via machine learning.

The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a r...

Machine learning based refined differential gene expression analysis of pediatric sepsis.

BACKGROUND: Differential expression (DE) analysis of transcriptomic data enables genome-wide analysi...

MGAT: Multi-view Graph Attention Networks.

Multi-view graph embedding is aimed at learning low-dimensional representations of nodes that captur...

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