Critical Care

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

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Subcategories: Sepsis
Showing 1303-1323 of 7,427 articles
Triplet-aware graph neural networks for factorized multi-modal knowledge graph entity alignment.

Multi-Modal Entity Alignment (MMEA), aiming to discover matching entity pairs on two multi-modal kno...

Machine learning for the prediction of in-hospital mortality in patients with spontaneous intracerebral hemorrhage in intensive care unit.

This study aimed to develop a machine learning (ML)-based tool for early and accurate prediction of ...

Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage.

BACKGROUND: Cerebral vasospasm (CV) is a feared complication which occurs after 20-40% of subarachno...

Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks.

Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of p...

NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset.

BACKGROUND AND OBJECTIVE: Breast cancer is a multifaceted condition characterized by diverse feature...

AMFP-net: Adaptive multi-scale feature pyramid network for diagnosis of pneumoconiosis from chest X-ray images.

Early detection of pneumoconiosis by routine health screening of workers in the mining industry is c...

Design and Implementation of an Intensive Care Unit Command Center for Medical Data Fusion.

The rapid advancements in Artificial Intelligence of Things (AIoT) are pivotal for the healthcare se...

Study of machine learning techniques for outcome assessment of leptospirosis patients.

Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical ...

Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.

Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Findin...

Multi-label classification of retinal diseases based on fundus images using Resnet and Transformer.

Retinal disorders are a major cause of irreversible vision loss, which can be mitigated through accu...

Multi-output neural network model for predicting biochar yield and composition.

In biomass pyrolysis for biochar production, existing prediction models face computational challenge...

Sepsis mortality prediction with Machine Learning Tecniques.

OBJECTIVE: To develop a sepsis death classification model based on machine learning techniques for p...

Dual-extraction modeling: A multi-modal deep-learning architecture for phenotypic prediction and functional gene mining of complex traits.

Despite considerable advances in extracting crucial insights from bio-omics data to unravel the intr...

Wastewater treatment process enhancement based on multi-objective optimization and interpretable machine learning.

Optimization and control of wastewater treatment process (WTP) can contribute to cost reduction and ...

Autism spectrum disorders detection based on multi-task transformer neural network.

Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in s...

Use of machine learning to identify protective factors for death from COVID-19 in the ICU: a retrospective study.

BACKGROUND: Patients in serious condition due to COVID-19 often require special care in intensive ca...

Epilepsy detection based on multi-head self-attention mechanism.

CNN has demonstrated remarkable performance in EEG signal detection, yet it still faces limitations ...

Identification of Respiratory Pauses during Swallowing by Unconstrained Measuring Using Millimeter Wave Radar.

Breathing temporarily pauses during swallowing, and the occurrence of inspiration before and after t...

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