Critical Care

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 dysregulated response to an infection, is one of the most common prediction tasks in intensive care-related machine learning research. To assess the performance of such models, different evaluation strategies, including fixed horizon (a single prediction at a set time before onset), peak score (a singl...

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 adverse clinical outcomes. Current extubation decisions rely heavily on weaning tests and subjective interpretation of chest radiographs. This study aimed to develop a clinically feasible multimodal machine learning (ML) framework that integrates routinely available data to provide complementary support ...

Integration of Systemic Immune-Inflammation Index and Lactate for diagnostic and early risk stratification in pediatric sepsis.

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 cause conflict between clinicians, patients and famili...

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 cardiovascular disease risk. We examined the multi-...

Digital twin technologies for supporting self-care in adults with diet-related chronic conditions: a systematic review.

BACKGROUND: Diet-related chronic conditions are major contributors to global morbidity and mortality. Effective management of these conditions require...

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 respiratory distress syndrome (ARDS) and their tran...

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 skeletal and cardiac muscle degeneration, loss of ambulat...

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 therapeutic approach in sepsis remain largely unexplored. Th...

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

Early prediction of Type 2 diabetes mellitus (T2DM) complications holds significant clinical importance for improving patient outcomes and reducing he...

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 sedative drugs can cause respiratory depression a...

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 appendage (LAA), exhibits hemodynamic properties ...

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 environmental risks that intensify population vulnerability. T...

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 cough acoustics and screening accuracy require 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 directly impact patient outcomes. While standardi...

Past, present and future perspectives of traumatic brain injury.

Despite extensive research, traumatic brain injury (TBI) imposes significant mortality and morbidity burden globally. This narrative review provides a...

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 dynamically evolving clinical presentation compli...

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 ill patients with gram-positive bacterial infection...

Machine learning-assisted Zr-MOFFe@SECHA fluorescence and colorimetric dual-mode intelligent platform for sensitive LPS detection.

BACKGROUND: Lipopolysaccharide (LPS) is a crucial diagnosis biomarker for sepsis. Accurate and sensitive determination of LPS in real samples (human s...