Intensivists

Speech-to-Speech Voice-Cloning Care (SVCC) for improving ICU-acquired anxiety for critically ill patients in a tertiary hospital in Beijing, China: protocol of a randomised, controlled trial.

INTRODUCTION: Intensive care unit (ICU) visiting restrictions in hospitals, implemented due to infection control and other factors, limited contact between patients and family members, affecting patients' well-being and clinical outcomes. With the evolution of voice-cloning based on artificial intelligence (AI) technology, it has become possible for health providers to communicate with critically ...

Accuracy, quality, and readability analyses of responses from large language models to questions on pediatric dental sedation.

Association between deep learning-based atrial fibrillation burden and in-hospital mortality.

Despite its clinical significance, research on atrial fibrillation (AF) burden as a dynamic, real-time predictor of adverse outcomes in patients with ...

Predictive accuracy of Early Warning Score Systems for Detecting Critically Ill Patients in an Outpatient Setting.

Background No systematic methods exist for triaging outpatients with severe conditions. Our previous pilot study suggested that the National Early War...

Development and multicenter validation of an explainable machine learning diagnostic criteria for pediatric abdominal sepsis.

Accurate identification of early pediatric abdominal sepsis (PAS) is essential to improving outcomes, yet most existing pediatric sepsis criteria and ...

Evaluating deep learning sepsis prediction models in ICUs under distribution shift: a multi-centre retrospective cohort study.

Sepsis prediction models trained on ICU data often fail to generalize under external validation because of distribution shift. Prior studies have focu...

Predicting short-term mortality in severe cirrhosis: An interpretable machine learning model integrating routine clinical indicators.

BACKGROUND: The critical need for precise risk stratification in severe liver cirrhosis is underscored by its substantial 30-day mortality rates, dema...

ARIMA-based forecasting of cerebral physiologic signals in acute traumatic brain injury: a CAnadian high-resolution TBI (CAHR-TBI) cohort study.

BACKGROUND: Traumatic brain injury (TBI) remains a major global health issue, with limited progress in reducing morbidity and mortality for TBI patien...

Deep learning-based labor relations prediction system with multi-source data fusion and early warning mechanisms.

Predicting workplace conflicts before they escalate into formal disputes or collective action represents a persistent challenge in organizational mana...

Exploring the Ethical Dimensions of AI-Driven Patient Monitoring in Critical Care Nursing: A Systematic Review.

BACKGROUND: Artificial intelligence (AI) is increasingly applied in intensive care units (ICUs) to enhance monitoring, prediction and decision-making....

AI-enhanced therapeutic drug monitoring for vancomycin and β-lactam antibiotics in critical care: from population PK to bedside algorithms.

INTRODUCTION: Although sepsis and other severe infections in intensive care are common and deadly, obtaining safe and effective exposure for vancomyci...

Fcer1g and St3gal1: Macrophage-associated angiogenesis biomarkers and therapeutic targets in sepsis-induced acute lung injury.

BACKGROUND: Acute lung injury (ALI) involves the release of growth factors and inflammatory mediators from damaged pulmonary tissues, fostering endoth...

Early hemodynamic phenotyping in sepsis using transthoracic echocardiography: A proof-of-concept study in a north African ICU.

BACKGROUND: Cardiovascular dysfunction in sepsis is heterogeneous and contributes to poor outcomes. Hemodynamic phenotyping may delineate pathophysiol...

Machine learning analysis of s-EASIX for predicting 30-day mortality in sepsis patients from MIMIC-IV.

Endothelial dysfunction is an important risk factor for the progression of sepsis. The simplified endothelial activation and stress index (s-EASIX) se...

Explainable AI for mortality prediction: a comparative study using the MIMIC-III dataset.

OBJECTIVES: Predicting mortality is vital for tailoring treatments, improving care and reducing costs. Machine learning (ML) has shown strong potentia...

Balancing Innovation in Artificial Intelligence With Practical Sepsis Definitions.

Operationalizing Sepsis Definitions With Artificial Intelligence: Toward Smarter Antibiotic Use.

Explainable multimodal deep learning models for variable-length sequences in critically ill patients.

OBJECTIVE: Deep learning models have shown strong performance in predicting clinical events in critical care using structured electronic health record...

Integrative transcriptomic and machine learning analysis identifies CDH17 and HOXC13 as robust candidate prognostic biomarkers in uveal melanoma.

BACKGROUND: Uveal melanoma (UM), the most common form of ocular melanoma, represents poor prognosis, with approximately 50% of patients developing met...

A Surface-Enhanced Raman Scattering-Digital Microfluidics Biosensing Platform Integrated with a 1D-Convolutional Neural Network for Noninvasive Detection of Inflammation Markers.

The noninvasive detection of multiple inflammatory markers (IMs) for early sepsis diagnosis is challenging. Therefore, herein, we innovatively develop...