Critical Care

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

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Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation.

We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound usin...

A novel multi-user collaborative cognitive radio spectrum sensing model: Based on a CNN-LSTM model.

Cognitive Radio (CR) technology enables wireless devices to learn about their surrounding spectrum e...

Fuzzy bifocal disambiguation for partial multi-label learning.

In partial multi-label learning (PML), each instance is associated with multiple candidate labels, b...

Machine learning and multi-omics in precision medicine for ME/CFS.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex and multifaceted disorder t...

Development of Predictive Model of Surgical Case Durations Using Machine Learning Approach.

Optimizing operating room (OR) utilization is critical for enhancing hospital management and operati...

MDFGNN-SMMA: prediction of potential small molecule-miRNA associations based on multi-source data fusion and graph neural networks.

BACKGROUND: MicroRNAs (miRNAs) are pivotal in the initiation and progression of complex human diseas...

Glo-net: A dual task branch based neural network for multi-class glomeruli segmentation.

Accurate segmentation and classification of glomeruli are fundamental to histopathology slide analys...

Multi-center brain age prediction via dual-modality fusion convolutional network.

Accurate prediction of brain age is crucial for identifying deviations between typical individual br...

Utilizing artificial intelligence and cellular population data for timely identification of bacteremia in hospitalized patients.

BACKGROUND: Bacteremia is a critical condition with high mortality that requires prompt detection to...

Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.

The aim of this study was to develop and validate a machine learning-based mortality risk prediction...

AI based medical imagery diagnosis for COVID-19 disease examination and remedy.

COVID-19, caused by the SARS-CoV-2 coronavirus, has spread to more than 200 countries, affecting mil...

Combining machine learning and single-cell sequencing to identify key immune genes in sepsis.

This research aimed to identify novel indicators for sepsis by analyzing RNA sequencing data from pe...

A comprehensive retrospect on the current perspectives and future prospects of pneumoconiosis.

Pneumoconiosis is a widespread occupational pulmonary disease caused by inhalation and retention of ...

Multi-site, multi-vendor development and validation of a deep learning model for liver stiffness prediction using abdominal biparametric MRI.

BACKGROUND: Chronic liver disease (CLD) is a substantial cause of morbidity and mortality worldwide....

Identifying Protein-Nucleotide Binding Residues via Grouped Multi-task Learning and Pre-trained Protein Language Models.

The accurate identification of protein-nucleotide binding residues is crucial for protein function a...

Development and external validation of a machine learning model for brain injury in pediatric patients on extracorporeal membrane oxygenation.

BACKGROUND: Patients supported by extracorporeal membrane oxygenation (ECMO) are at a high risk of b...

Multi-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model.

Human mobility between different regions is a major factor in large-scale outbreaks of infectious di...

Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit.

INTRODUCTION: Delirium, frequently experienced by ischemic stroke patients, is one of the most commo...

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