Critical Care

Letter to the Editor: "Evaluating the efficacy of large language models in predicting intensive care unit admission needs".

This letter comments on the study by Turan et al., which evaluates the efficacy of large language mo...

The Impact of Evaluation Strategy on Sepsis Prediction Model Performance Metrics in Intensive Care Data: Retrospective Cohort Study.

BACKGROUND: The prediction of the onset of sepsis, a life-threatening condition resulting from a dys...

Extubation Decision Support in Critical Care: A Multimodal Machine Learning Framework Integrating Segmented Radiographs and Routine Clinical Data.

Extubation failure remains a major challenge in critically ill patients and is associated with adver...

How ICU clinicians document 'Futility': A 10-year analysis of critical care notes using natural language processing notes.

BACKGROUND: ICU patients frequently receive treatments clinicians perceive are futile which can caus...

Combined variations in sleep, physical activity, and nutrition and the risk of major adverse cardiovascular events.

AIMS: Sleep, physical activity, and nutrition (SPAN) are major lifestyle behaviours that influence c...

Longitudinal Subphenotyping of Acute Respiratory Distress Syndrome: Implications in Prognostic Prediction and Clinical Intervention.

BACKGROUND AND OBJECTIVE: To identify the longitudinal subphenotypes (LSPs) in patients with acute r...

Letter to the Editor: Nocturnal enteral feeding and mechanical ventilation risk in intensive care unit patients: A deep Learning-Based causal inference study.

Targeting autophagy in Duchenne muscular dystrophy: mechanistic insights and emerging therapeutic strategies.

Duchenne muscular dystrophy (DMD) is a severe X-linked myopathy characterised by progressive skeleta...

Identification and experimental validation of biomarkers associated with integrated stress response in sepsis.

BACKGROUND: Strategies that specifically target the integrated stress response (ISR) as a therapeuti...

Graph-Enhanced Multi-Task Learning for Type 2 Diabetes Comorbidity Risk Prediction.

Early prediction of Type 2 diabetes mellitus (T2DM) complications holds significant clinical importa...

Utilization and Feasibility of a Wearable Device in Patients With Sedative Effects of Drugs: Protocol for a Prospective Observational Study for the Advanced Respiratory Monitoring Events in Drug Toxicity (ARM-ED) Study.

BACKGROUND: Drug-related deaths worldwide are most commonly attributed to opioids. Opioids and other...

Interpretable Clinical-Radiomics Model for Prediction of Blood Stasis and Left Atrial Appendage Thrombus.

BACKGROUND: The left atrial (LA) morphological profile, anatomically contiguous with the left atrial...

Analysis of Models to Estimate Morbidity Rates of Respiratory Diseases Through Deep Learning.

Respiratory diseases remain a challenge in Brazil due to socioeconomic inequalities and environmenta...

Cough acoustic analysis using artificial intelligence for COVID-19 detection: A comparative study of patient cohorts from Lima, Peru and Montreal, Canada.

PURPOSE: Digital cough screening for COVID-19 detection shows promise, but population differences in...

DispatchMAS: fusing taxonomy and artificial intelligence agents for emergency medical services.

BACKGROUND: Emergency medical dispatch is a critical, high-stakes process where dispatcher decisions...

Past, present and future perspectives of traumatic brain injury.

Despite extensive research, traumatic brain injury (TBI) imposes significant mortality and morbidity...

Prediction-guided clustering for sepsis phenotyping: a retrospective cohort analysis.

BACKGROUND: Sepsis is a major cause of morbidity and mortality worldwide, with its heterogeneous and...

Development and validation of an artificial intelligence-based model for predicting teicoplanin plasma concentrations in intensive care unit patients with pulmonary infections: a retrospective study.

INTRODUCTION: Teicoplanin is widely used to treat pulmonary infections, particularly in critically i...

Representation learning for the prediction of length of stay in infants with congenital heart diseases undergoing cardiac surgery.

AIMS: Congenital heart disease is the most common birth defect and significantly impacts health, mor...

Artificial Intelligence-Driven Electrocardiogram Screening for Asymptomatic Left Ventricular Systolic Dysfunction in the General Population.

BACKGROUND: Asymptomatic left ventricular systolic dysfunction (LVSD) is a well-established precurso...

DeePedIMV: Deep learning model for early prediction of invasive mechanical ventilation in critically ill children.

BACKGROUND: Acute deterioration requiring invasive mechanical ventilation (IMV) is common in critica...