Critical Care

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

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Subcategories: Sepsis
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Mortality prediction after major surgery in a mixed population through machine learning: a multi-objective symbolic regression approach.

INTRODUCTION: Understanding 1-year mortality following major surgery offers valuable insights into p...

AI-based models to predict decompensation on traumatic brain injury patients.

Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in tempo...

Two-stage Non-Intrusive Load Monitoring method for multi-state loads.

The loads that have several working states cannot be accurately distinguished by the conventional No...

DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.

INTRODUCTION: Heart failure (HF) has a very high prevalence in patients with maintenance hemodialysi...

Interpretable machine learning-based prediction of 28-day mortality in ICU patients with sepsis: a multicenter retrospective study.

BACKGROUND: Sepsis is a major cause of mortality in intensive care units (ICUs) and continues to pos...

Abductive multi-instance multi-label learning for periodontal disease classification with prior domain knowledge.

Machine learning is widely used in dentistry nowadays, offering efficient solutions for diagnosing d...

Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network for ERP Detection.

Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative m...

Prediction of mortality in intensive care unit with short-term heart rate variability: Machine learning-based analysis of the MIMIC-III database.

BACKGROUND: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiologi...

MO-GCN: A multi-omics graph convolutional network for discriminative analysis of schizophrenia.

The methodology of machine learning with multi-omics data has been widely adopted in the discriminat...

Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images.

In the present scenario, cancerous tumours are common in humans due to major changes in nearby envir...

Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.

Although prediction models for heart transplantation outcomes have been developed previously, a comp...

An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia.

BACKGROUND: Ventilator-associated pneumonia (VAP) is a common nosocomial infection in ICU, significa...

Accurate multi-behavior sequence-aware recommendation via graph convolution networks.

How can we recommend items to users utilizing multiple types of user behavior data? Multi-behavior r...

rU-Net, Multi-Scale Feature Fusion and Transfer Learning: Unlocking the Potential of Cuffless Blood Pressure Monitoring With PPG and ECG.

This study introduces an innovative deep-learning model for cuffless blood pressure estimation using...

Interpretable Multi-Branch Architecture for Spatiotemporal Neural Networks and Its Application in Seizure Prediction.

Currently, spatiotemporal convolutional neural networks (CNNs) for electroencephalogram (EEG) signal...

A Trustworthy Curriculum Learning Guided Multi-Target Domain Adaptation Network for Autism Spectrum Disorder Classification.

Domain adaptation has demonstrated success in classification of multi-center autism spectrum disorde...

Self-Supervised Contrastive Learning on Attribute and Topology Graphs for Predicting Relationships Among lncRNAs, miRNAs and Diseases.

Exploring associations between long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is cr...

MFRC-Net: Multi-Scale Feature Residual Convolutional Neural Network for Motor Imagery Decoding.

Motor imagery (MI) decoding is the basis of external device control via electroencephalogram (EEG). ...

Residual Self-Calibrated Network With Multi-Scale Channel Attention for Accurate EOG-Based Eye Movement Classification.

Recently, Electrooculography-based Human-Computer Interaction (EOG-HCI) technology has gained widesp...

A novel swarm budorcas taxicolor optimization-based multi-support vector method for transformer fault diagnosis.

To address the challenge of low recognition accuracy in transformer fault detection, a novel method ...

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