Critical Care

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

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Subcategories: Sepsis
Showing 43-63 of 7,402 articles
Integrative network pharmacology and multi-omics reveal anisodamine hydrobromide's multi-target mechanisms in sepsis.

Sepsis, marked by hyperinflammation and subsequent immunosuppression, lacks effective phase-specific...

Dominant Classifier-assisted Hybrid Evolutionary Multi-objective Neural Architecture Search.

Neural Architecture Search (NAS) automates the design of deep neural networks but remains computatio...

Explainable machine learning for predicting ICU mortality in myocardial infarction patients using pseudo-dynamic data.

Myocardial infarction (MI) remains one of the greatest contributors to mortality, and patients admit...

Measuring intensive care performance to attain collaborative quality improvement: Work in progress but much to be done.

Intensive Care Medicine must be viewed as an organized system of care that ensures delivery of timel...

Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review.

INTRODUCTION: The use of machine learning (ML) in intensive care units (ICUs) has led to a large yet...

Developing and validating machine learning models to predict next-day extubation.

Criteria to identify patients who are ready to be liberated from mechanical ventilation (MV) are imp...

MFFBi-Unet: Merging Dynamic Sparse Attention and Multi-scale Feature Fusion for Medical Image Segmentation.

The advancement of deep learning has driven extensive research validating the effectiveness of U-Net...

Interpretable graph Kolmogorov-Arnold networks for multi-cancer classification and biomarker identification using multi-omics data.

The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge...

Diagnosis of Major Depressive Disorder Based on Multi-Granularity Brain Networks Fusion.

Major Depressive Disorder (MDD) is a common mental disorder, and making an early and accurate diagno...

Enhancing EEG-Based Schizophrenia Diagnosis with Explainable Multi-Branch Deep Learning.

Schizophrenia poses diagnostic challenges due to a lack of objective assessment. We propose MBSzEEGN...

Determination of milk yield in water buffaloes using multi-class logistic regression and machine learning methods.

In this study, Random Forest, Gradient Boosting Machines (GBM), and Support Vector Machines (SVM), M...

Effect of artificial intelligence in extracorporeal membrane oxygenation: a systematic review and meta-analysis.

OBJECTIVES: To evaluate the effectiveness of Artificial Intelligence (AI) in improving clinical outc...

Development and validation of machine learning predictive models for assessing dialysis adequacy in dialysis patients.

PURPOSE: The assessment of dialysis adequacy is of great clinical importance. However, it depends on...

Multi-modal classification of retinal disease based on convolutional neural network.

Retinal diseases such as age-related macular degeneration and diabetic retinopathy will lead to irre...

Identifying key physiological and clinical factors for traumatic brain injury patient management using network analysis and machine learning.

In the intensive care unit (ICU), managing traumatic brain injury (TBI) patients presents significan...

Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson's disease.

Cognitive impairment is a frequent complication of Parkinson's disease (PD), affecting up to half of...

Development and validation of machine learning-based risk prediction models for ICU-acquired weakness: a prospective cohort study.

BACKGROUND: Intensive care unit (ICU)-acquired weakness (ICUAW) is a prevalent complication in criti...

Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning.

BACKGROUND: In the Intensive Care Unit (ICU), data stored in patient data management systems (PDMS) ...

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