Critical Care

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, requiring dialysis treatment or kidney transplantation. The endothelial activation and stress index (EASIX), reflecting inflammatory status and endothelial dysfunction, has demonstrated predictive value for outcomes in multiple diseases. However, its association with in-hospital mortality (IHM) risk ...

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 teaching provides a feasible pathway for achieving deep learning. This study aimed to verify the efficacy of a deep learning-based blended teaching model integrating online immersive learning, offline scenario simulation and clinical case review in improving ECMO transport competency among ICU nurses...

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

BACKGROUND: Conventional clinical scoring systems and contrast-enhanced computed tomography (CECT) interpretation provide limited accuracy in predicti...

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 and can be captured using consumer-grade smartphon...

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. Traditional evidence‑based nursing is limited in personaliz...

Machine learning-assisted self-powered ear tag for animal welfare.

Metabolic health serves as a crucial indicator of animal welfare, yet nutritional imbalances in intensive farming diets often induce metabolic dysregu...

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 mechanistic connections between real-world chemical mi...

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 critically ill patients and may lead to atelectasis o...

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

Nanostructured electrode materials and flexible-substrate engineering for wearable multi-analyte biosensors in diabetes monitoring and personalized care: a comprehensive review.

This review examines the convergence of wearable biosensors and artificial intelligence (AI) in personalized diabetes care. It addresses the limitatio...

Estimating a Physiological Lung Function Score and Biological Sex Using Pulmonary Function Tests and Machine Learning: Retrospective Study.

BACKGROUND: Sex and age have long been known to affect lung function. Several biological variables and anatomical factors may contribute to sex- and a...

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 (EHRs) by extracting mortality-related high-risk cl...

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. Machine learning (ML) applied to electronic medical re...

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 intermittent behavioral scales like the Richmond Agi...

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 regarding artificial intelligence (AI) in intensive...

HAMNet: Hierarchical Multi-scale Attention Network for precise disease detection in pearl millet using spatial fusion.

Pearl millet is a vital crop in arid and semi-arid regions, but its productivity is significantly impacted by fungal and bacterial diseases. Tradition...

MRI-based deep learning radiomics model for automated classification of disc degeneration in the lumbar spine.

Disc degeneration in the lumbar spine is a major cause of low back pain (LBP). The accurate grading of disc degeneration on magnetic resonance imaging...

Evaluating Elicit's systematic reviews workflow in an umbrella review on air pollution and acute lower respiratory infections: a methodological study for quality appraisal.

BACKGROUND: The exponential growth of scientific publications has increased the complexity of evidence synthesis. Systematic reviews remain essential ...

Association between the development of sepsis and the BAR index in patients with acute pancreatitis: A retrospective investigation based on MIMIC-IV database.

Sepsis, as a severe complication of acute pancreatitis (AP), needs to be identified and treated as early as possible. The blood urea nitrogen-to-album...

Multi-task deep learning for sub-clinical screening of mood and sleep disturbances using physical activity biomarkers.

BACKGROUND: Sub-clinical screening for mood disorders and sleep disturbances remains challenging due to limited accessibility of mental health screeni...