Critical Care

Artificial intelligence in predicting anesthetic complications: current techniques, clinical applications, and limitations.

Artificial intelligence (AI) is revolutionizing anesthesiology by enhancing the prediction and manag...

Accurate prediction of mortality in children with sepsis: development and validation of an explainable model based on real-world data.

BACKGROUND: Sepsis remains the leading cause of in-hospital deaths among children, and there is curr...

Operational Integration and Temporal Validation of a Continuously Deployed ICU Prediction Model.

OBJECTIVES: To operationalize and temporally validate an electronic medical record (EMR)-integrated ...

Artificial intelligence in paediatric chest imaging: applications, challenges, and future directions.

BACKGROUND: Paediatric chest imaging is central to diagnosing respiratory and cardiopulmonary diseas...

Development and Interpretability Analysis of a Stacking Ensemble Model for Early Prediction of Nutritional Risk in Intensive Care Unit Patients: Retrospective Cohort Study.

BACKGROUND: Malnutrition in critically ill patients is associated with increased morbidity and morta...

Integration of single-cell and bulk RNA sequencing reveals programmed cell death-associated transcriptional programs in sepsis-induced acute lung injury.

BACKGROUND: Sepsis-induced acute lung injury (ALI) is a frequent and life-threatening complication o...

Predicting respiratory infection and symptoms development using gene set enrichment scores and machine learning.

Recent advancements in precision medicine enable personalized predictions grounded in individual-lev...

AI-based prediction of aspirin-exacerbated respiratory disease using nasal epithelial mRNA expression profiles.

BACKGROUND: Aspirin-exacerbated respiratory disease (AERD) is a distinct asthma endotype marked by a...

Endothelial activation and stress index associated with in-hospital mortality risk in patients with end-stage renal disease: a retrospective analysis based on the MIMIC database and machine learning model development.

BACKGROUND: End-stage renal disease (ESRD) is a severe chronic renal disorder with high mortality, r...

Training effect of a deep learning-based blended teaching model on ECMO transport for ICU nurses: a prospective, parallel-group, randomized controlled trial.

OBJECTIVE: Given the high alignment between deep learning and blended teaching objectives, blended t...

Multimodal AI for early prediction of adverse clinical outcomes in acute pancreatitis.

BACKGROUND: Conventional clinical scoring systems and contrast-enhanced computed tomography (CECT) i...

On-device cough detection and respiratory disease classification enhanced by generative data augmentation.

BACKGROUND: Cough sounds are accessible, non-invasive biomarkers for respiratory disease assessment ...

The effect of artificial intelligence assisted evidence-based nursing on psychological distress relief and nursing satisfaction of patients with respiratory diseases.

BACKGROUND: Patients with respiratory diseases commonly experience psychological distress. Tradition...

Plasticizer-responsive molecular axes in heart failure: a subtype-aware toxicogenomic framework relevant to environmental health.

Environmental exposure to synthetic chemicals has been associated with cardiovascular risk, yet mech...

Identification of Pathological Secretions in Bronchoscopic Images Based on Edge Features and Multi-Scale Attention Mechanism.

The accumulation of pathological bronchial secretions compromises ventilation and oxygenation in cri...

Monitoring What Matters: Artificial Intelligence, Inspiratory Effort, and Patient-Ventilator Interaction in the ICU.

HiRMD: A System for Mortality Prediction via LLM-Based High-Risk Information Extraction and Diagnosis.

OBJECTIVE: To improve in-hospital mortality prediction from longitudinal electronic health records (...

AI for predicting exacerbations in KIDs with asthma (AIRE-KIDS).

Recurrent acute care visits are a common yet preventable outcome for many children with asthma. Mach...

A Hierarchical Multimodal Framework for Sedation Monitoring in ICU Patients.

In the intensive care unit (ICU), monitoring sedation levels is crucial. Clinicians often rely on in...

Perceptions of Intensive Care Nurses Toward Artificial Intelligence Technologies: A Qualitative Study.

Intensive care nurses play a pivotal role in patient care; however, their perceptions and concerns r...

Memory-augmented graph neural networks for multi-class cardiovascular disease recognition from 12-lead ECG signals.

BACKGROUND AND OBJECTIVE: Cardiovascular disease recognition from electrocardiogram (ECG) requires c...