Critical Care

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

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Subcategories: Sepsis
Showing 3844-3864 of 7,492 articles
Optimizing ICU Care: Machine Learning and PCA for Early Prediction of Renal Replacement Therapy Requirement.

Forecasting the need for Renal Replacement Therapy (RRT) in intensive care units (ICUs) at an early ...

Multi-Objective Performance Optimization of Machine Learning Models in Healthcare.

Multi-objective optimization holds particular significance for medical applications, wherein enhanci...

A Comparative Analysis of Federated and Centralized Learning for SpO2 Prediction in Five Critical Care Databases.

This study explores the potential of federated learning (FL) to develop a predictive model of hypoxe...

Machine Learning to Predict the Risk of Malnutrition in Hospitalized Patients with Pneumonia and Analysis of Related Prognostic Factor.

This study explored machine learning's potential in predicting the nutritional status and outcomes f...

User-Centered Development of Explanation User Interfaces for AI-Based CDSS: Lessons Learned from Early Phases.

This paper reports lessons learned during the early phases of the user-centered design process for a...

Protein multi-level structure feature-integrated deep learning method for mutational effect prediction.

Through iterative rounds of mutation and selection, proteins can be engineered to enhance their desi...

Development of a Mapping Table for Nursing Notes Based on Nurses' Concerns in ICU Patients.

This study aimed to develop a mapping table that connects nursing notes with standard terminology, f...

Machine Learning-Based Prediction Models of Mortality for Intensive Care Unit Patients Using Nursing Records.

This study aimed to develop ICU mortality prediction models using a conceptual framework, focusing o...

Identification and validation of potential genes for the diagnosis of sepsis by bioinformatics and 2-sample Mendelian randomization study.

This integrated study combines bioinformatics, machine learning, and Mendelian randomization (MR) to...

Advancing Chest X-ray Diagnostics via Multi-Modal Neural Networks with Attention.

The healthcare field is undergoing a profound shift, with deep learning in AI increasingly augmentin...

A Resistance-Free Sit-To-Stand Rehabilitative System Incorporated with Multi-Sensory Feedback.

Robotic rehabilitative systems have been an active area of research for all movements, including Sit...

Improving Neonatal Care with AI: Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants.

In this study, we developed an AI model to predict Respiratory Distress Syndrome (RDS) in premature ...

Machine Learning Model Combining Ventilatory, Hypoxic, Arousal Domains Across Sleep Better Predicts Adverse Consequences of Obstructive Sleep Apnea.

Obstructive sleep apnea(OSA) severity is currently assessed clinically using the apnea-hypopnea inde...

Cough Classification of Unknown Emerging Respiratory Disease with Federated Learning.

Artificial intelligence offers great potential to address the need for rapid diagnostic testing in p...

TAU-DI Net: A Multi-Scale Convolutional Network Combining Prob-Sparse Attention for EEG-based Depression Identification.

EEG-based detection of major depression disorder (MDD) plays a pivotal role in the subsequent treatm...

Audio Cough Analysis by Parametric Modelling of Weighted Spectrograms to Interpret the Output of Convolutional Neural Networks.

This study explores the feasibility of employing eXplainable Artificial Intelligence (XAI) methodolo...

Towards Personalized Inhalation Therapy by Correlating Chest CT Imaging and Pulmonary Function Test Features Using Machine Learning.

Inhalation therapy is the predominant method of treatment for a variety of respiratory diseases. The...

Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data.

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for...

A Deep-Learning-Based Approach for Delirium Monitoring in ICU Patients Using Thermograms.

Patients in the ICU frequently suffer from delirium, which can delay their recovery and may cause si...

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