Critical Care

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

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Subcategories: Sepsis
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Research of multi-label text classification based on label attention and correlation networks.

Multi-Label Text Classification (MLTC) is a crucial task in natural language processing. Compared to...

Transfer learning-enabled outcome prediction for guiding CRRT treatment of the pediatric patients with sepsis.

Continuous renal replacement therapy (CRRT) is a life-saving procedure for sepsis but the benefit of...

Deep-learning model accurately classifies multi-label lung ultrasound findings, enhancing diagnostic accuracy and inter-reader agreement.

Despite the increasing use of lung ultrasound (LUS) in the evaluation of respiratory disease, operat...

Multi-relational graph contrastive learning with learnable graph augmentation.

Multi-relational graph learning aims to embed entities and relations in knowledge graphs into low-di...

A machine learning-based electronic nose for detecting neonatal sepsis: Analysis of volatile organic compound biomarkers in fecal samples.

BACKGROUND: Neonatal sepsis is a global health threat, contributing to high morbidity and mortality ...

Multi-source Selective Graph Domain Adaptation Network for cross-subject EEG emotion recognition.

Affective brain-computer interface is an important part of realizing emotional human-computer intera...

TW-YOLO: An Innovative Blood Cell Detection Model Based on Multi-Scale Feature Fusion.

As deep learning technology has progressed, automated medical image analysis is becoming ever more c...

Development and validation of a machine-learning model for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage.

Pneumonia is a common postoperative complication in patients with aneurysmal subarachnoid hemorrhage...

Multi-scale convolution enhanced transformer for multivariate long-term time series forecasting.

In data analysis and forecasting, particularly for multivariate long-term time series, challenges pe...

Explainable machine learning and online calculators to predict heart failure mortality in intensive care units.

AIMS: This study aims to develop explainable machine learning models and clinical tools for predicti...

Extracorporeal Closed-Loop Respiratory Regulation for Patients With Respiratory Difficulty Using a Soft Bionic Robot.

OBJECTIVE: Respiratory regulation is critical for patients with respiratory dysfunction. Clinically ...

PPG2RespNet: a deep learning model for respirational signal synthesis and monitoring from photoplethysmography (PPG) signal.

Breathing conditions affect a wide range of people, including those with respiratory issues like ast...

SSR-DTA: Substructure-aware multi-layer graph neural networks for drug-target binding affinity prediction.

Accurate prediction of drug-target binding affinity (DTA) is essential in the field of drug discover...

A Multi-Omics, Machine Learning-Aware, Genome-Wide Metabolic Model of Bacillus Subtilis Refines the Gene Expression and Cell Growth Prediction.

Given the extensive heterogeneity and variability, understanding cellular functions and regulatory m...

Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study.

BACKGROUND: Cardiac arrest (CA) is one of the leading causes of death among patients in the intensiv...

Criticality of Nursing Care for Patients With Alzheimer's Disease in the ICU: Insights From MIMIC III Dataset.

Alzheimer's disease (AD) patients admitted to intensive care units (ICUs) exhibit varying survival o...

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