Critical Care

Pre-Imaging Clinical Factors Associated With Cardiac MR Image Quality Using Large Language Model-Enabled Data Extraction.

BACKGROUND: Poor cardiac MR image quality can prompt repeat examinations and hinder clinical decisio...

A motor thalamic site in humans that suppresses involuntary breathing without awareness.

Breathing is generated by brainstem respiratory networks but can be controlled and modulated by fore...

AttnEffNet-B4: an attention-augmented EfficientNet-B4 framework with fourier transformation for robust multi-disease diagnosis.

Early and precise disease detection in healthcare plays a vital role for better treatment results, h...

ECMO PAL VV: using deep neural networks for survival prognostication in venovenous extracorporeal membrane oxygenation.

Predicting Postoperative Cognitive Dysfunction in Older Cardiac Surgery Patients: An Integrated Machine Learning Approach With a Visual Nomogram.

OBJECTIVES: To integrate machine learning algorithms to identify key risk factors for postoperative ...

Development of a real-world, therapeutic drug monitoring-informed model to predict teicoplanin daily dose in pediatric intensive care unit patients with bacterial infections.

INTRODUCTION: Teicoplanin is commonly used to treat Gram-positive bacterial infections in the intens...

DeepCRI: Real-time EEG-based Prognostication after Cardiac Arrest.

Accurate prediction of neurological outcome after cardiac arrest is essential for guiding intensive ...

Cross-sectional research: Application of an Artificial Intelligence-Based Pediatric Early Warning Score in the Pediatric Emergency Department.

BACKGROUND: There are a large number of pediatric emergency patients. Due to the fact that the child...

Multimodal cardiovascular risk profiling using self-supervised learning of polysomnography.

STUDY OBJECTIVES: Polysomnography (PSG) provides a comprehensive assessment of brain, cardiac, and r...

Construction and interpretability evaluation of a prediction model for radiographic pneumonia in children at outpatient and emergency departments.

BACKGROUND: Radiographic confirmation is crucial for pediatric pneumonia diagnosis, but chest X-ray ...

Natural bioactive as modulators of airway inflammation and remodeling: mechanistic insights.

Airway diseases such as asthma, COPD, pulmonary fibrosis, and other inflammatory airway conditions r...

Targeted Surface-Enhanced Raman Scattering for Highly Accurate Identification of Bacterial Species and Finding Spectral Signatures with Explainable Artificial Intelligence.

The analysis of complex Raman spectra from biological samples has traditionally relied on convention...

Early detection of heart failure using trend similarity metrics combined with hybrid deep learning based on real-world health monitoring data.

Heart failure (HF) is a global health crisis, with over 18 million annual deaths attributed to cardi...

Predicting Postoperative Outcomes in Pediatric Ureteroscopy Using Machine Learning and Explainable AI-EAU Endourology Vision-AI Study.

AIM OF THE STUDY: This study aimed to evaluate the performance of machine learning (ML) algorithms i...

Nephrology: What You May Have Missed in 2025.

This article highlights some important nephrology studies published in 2025 that are relevant for ma...

Novel machine learning-based approach to identify viral biomarkers of human respiratory emissions from oral and nasal metagenomes.

Humans spend approximately 90% of their lives in built environments, making virus transmission indoo...

In-hospital outcomes and cost-effectiveness of transcatheter aortic valve replacement among younger patients: a double/debiased machine learning approach using electronic health records in Germany.

BACKGROUND: The prevalence of severe symptomatic aortic stenosis is increasing with population aging...

Risk prediction in spine surgery: a scoping review of traditional models, artificial intelligence, and the challenge of clinical translation.

BACKGROUND: Accurate perioperative risk stratification is essential to patient safety and informed c...

Performance of Machine Learning Models for Sepsis and Stroke Detection Using EMS Data.

OBJECTIVES: Early sepsis and stroke recognition by emergency medical services (EMS) improves triage,...

Explainable machine learning for predicting mechanical ventilation in Alzheimer's patients with pneumonia: a SHAP-guided XGBoost nomogram based on MIMIC-IV.

Alzheimer's disease (AD) patients are particularly vulnerable to pneumonia and subsequent respirator...

[Expert consensus on optimizing the multidisciplinary clinical pathways and management models for pulmonary function testing].

The promotion and application of pulmonary function tests (PFTs) in China have achieved preliminary ...