Critical Care

MRI-based risk factors for intensive care unit admissions in acute neck infections.

OBJECTIVES: We assessed risk factors and developed a score to predict intensive care unit (ICU) admissions using MRI findings and clinical data in acute neck infections.

Effective evaluation of greenhouse gases (GHGs) emissions from anoxic/oxic (A/O) process of regenerated papermaking wastewater treatment through hybrid deep learning techniques: Leveraging the critical role of water quality indicators.

Accurate accounting of greenhouse gases (GHGs) emissions from industrial wastewater treatment processes/plants with high organic concentration and fluctuating inflows is crucial for the calculation and management of carbon emissions. The impacts of water quality indicators on GHGs emissions within the biological nutrient removal process are still unclear, which deserves intensive attention. Here, ...

Harness machine learning for multiple prognoses prediction in sepsis patients: evidence from the MIMIC-IV database.

BACKGROUND: Sepsis, a severe systemic response to infection, frequently results in adverse outcomes, underscoring the urgency for prompt and accurate ...

A weakly supervised deep learning framework for automated PD-L1 expression analysis in lung cancer.

The growing application of immune checkpoint inhibitors (ICIs) in cancer immunotherapy has underscored the critical need for reliable methods to ident...

Separating obstructive and central respiratory events during sleep using breathing sounds: Utilizing transfer learning on deep convolutional networks.

Sleep apnea diagnosis relies on polysomnography (PSG), which is resource-intensive and requires manual analysis to differentiate obstructive sleep apn...

Prognostic value of SAPS II score for 28-day mortality in ICU patients with acute pulmonary embolism.

BACKGROUND: Acute pulmonary embolism (APE) is a common and life-threatening emergency in intensive care units (ICUs). Effective risk assessment tools ...

Amogel: a multi-omics classification framework using associative graph neural networks with prior knowledge for biomarker identification.

The advent of high-throughput sequencing technologies, such as DNA microarray and DNA sequencing, has enabled effective analysis of cancer subtypes an...

Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings.

BACKGROUND: Spontaneous intracerebral hemorrhage (SICH) is a devastating condition that significantly contributes to high mortality rates. This study ...

Machine learning prediction models for multidrug-resistant organism infections in ICU ventilator-associated pneumonia patients: Analysis using the MIMIC-IV database.

OBJECTIVE: This study aims to construct and compare four machine learning models using the MIMIC-IV database to identify high-risk factors for multidr...

Constructing an early warning model for elderly sepsis patients based on machine learning.

Sepsis is a serious threat to human life. Early prediction of high-risk populations for sepsis is necessary especially in elderly patients. Artificial...

Large Language Model-Driven Knowledge Graph Construction in Sepsis Care Using Multicenter Clinical Databases: Development and Usability Study.

BACKGROUND: Sepsis is a complex, life-threatening condition characterized by significant heterogeneity and vast amounts of unstructured data, posing s...

AI-powered model for predicting mortality risk in VA-ECMO patients: a multicenter cohort study.

Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is a critical life support technology for severely ill patients. Despite its benefits, pat...

Deep neural networks excel in COVID-19 disease severity prediction-a meta-regression analysis.

COVID-19 is a disease in which early prognosis of severity is critical for desired patient outcomes and for the management of limited resources like i...

Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms.

INTRODUCTION: Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is essential to prevent postoperative complications and optimize...

Machine Learning-Based VO Estimation Using a Wearable Multiwavelength Photoplethysmography Device.

The rate of oxygen consumption, which is measured as the volume of oxygen consumed per mass per minute (VO) mL/kg/min, is a critical metric for evalua...

Explainable SHAP-XGBoost models for pressure injuries among patients requiring with mechanical ventilation in intensive care unit.

pressure injuries are significant concern for ICU patients on mechanical ventilation. Early prediction is crucial for enhancing patient outcomes and r...

Student dropout prediction through machine learning optimization: insights from moodle log data.

Student attrition and academic failure remain pervasive challenges in education, often occurring at substantial rates and posing considerable difficul...

Artificial Intelligence-Guided Bronchoscopy is Superior to Human Expert Instruction for the Performance of Critical-Care Physicians: A Randomized Controlled Trial.

OBJECTIVES: Bronchoscopy in the mechanically ventilated patient is an important skill for critical-care physicians. However, training opportunity is h...

PyGlaucoMetrics: A Stacked Weight-Based Machine Learning Approach for Glaucoma Detection Using Visual Field Data.

: Glaucoma (GL) classification is crucial for early diagnosis and treatment, yet relying solely on stand-alone models or International Classification ...

Enhanced machine learning predictive modeling for delirium in elderly ICU patients with COPD and respiratory failure: A retrospective study based on MIMIC-IV.

BACKGROUND AND OBJECTIVE: Elderly patients with Chronic obstructive pulmonary disease (COPD) and respiratory failure admitted to the intensive care un...