Critical Care

Toward transparent clinical decision support in the ICU: A multi-window, model-agnostic explainability approach for 72-h mortality prediction using the eICU collaborative research database.

OBJECTIVE: This study has two main objectives: (1) to develop a multi-model framework for predicting...

LLM-based pedagogical agent for ICU simulation instructor training: A quasi-experimental study.

BACKGROUND: Intensive Care Unit (ICU) nursing is demanding, requiring advanced clinical decision-mak...

Multi-teacher knowledge distillation framework for lightweight anomaly detection.

Anomaly detection is essential in various domains, where identifying rare and irregular samples is c...

Takotsubo cardiomyopathy: Advances in pathophysiology, diagnostic biomarkers, genetic insights, multisystemic involvement, treatment updates & multidisciplinary interventions.

BACKGROUND: Takotsubo cardiomyopathy (TTC) is an acute, reversible cardiac syndrome triggered by phy...

Machine Learning Based Prediction of 28-Day Mortality in ECMO Patients: A Pilot Study Using MIMIC-IV Database.

BackgroundExtracorporeal membrane oxygenation (ECMO) is a critical life-sustaining intervention for ...

Perioperative Extracorporeal Membrane Oxygenation Use in Lung Transplantation: Analysis of Outcomes and Future Directions.

Extracorporeal membrane oxygenation (ECMO) has emerged as a critical intervention in the management ...

"PD-PREDICT": A Machine Learning Model for Patient Survival in Peritoneal Dialysis.

Accurate survival prediction in peritoneal dialysis (PD) patients is essential for personalized trea...

Robotic thoracic surgery: what is different in anesthesia?

PURPOSE OF REVIEW: Robotic-assisted thoracic surgery (RATS) has emerged as a transformative approach...

Development of an Explainable Machine Learning Model to Predict Mortality Risk in Sepsis Patients: Insights From a Real-World Clinical Data.

BACKGROUND: Sepsis is a life-threatening dysregulated host response to infection; early risk stratif...

Identification and Validation of Key Genes Related to Arginine Methylation Modification in Sepsis Using Transcriptome Combined with Mendelian Randomization Analysis.

BACKGROUND: Patients with sepsis often exhibit a decrease in lymphatic numbers, which can be facilit...

Hybridized artificial intelligence system for reducing neonatal mortality in Nigeria.

BACKGROUND: Neonatal diseases represent the leading cause of death in Nigeria, ranking the country s...

Artificial intelligence-based models for clinical prognosis at the end of life in the ICU.

INTRODUCTION: Artificial intelligence (AI) has emerged as a promising tool to enhance clinical predi...

Hospital-acquired pneumonia: Past, present, and perspectives.

Hospital-acquired pneumonia (HAP) remains the most frequent and lethal hospital acquired infection, ...

Artificial intelligence in ECMO management.

Artificial intelligence in paediatric respiratory medicine.

Proponents of artificial intelligence (AI) believe that it will revolutionise the modern world, affe...

Sysmex Cell Population Data for Diagnosing Infection in Patients With Suspected Sepsis in the Emergency Department.

OBJECTIVES: Early diagnosis of suspected sepsis is crucial to improve patient survival. Cell populat...

Interpretable artificial intelligence to predict preoperative risk factors for failure to rescue after coronary artery bypass grafting.

OBJECTIVE: Failure to rescue (FTR) is a significant quality indicator for postoperative cardiothorac...

Ahnak and Nckap1l as potential diagnostic biomarkers and therapeutic targets in Landiolol-mediated sepsis treatment.

PURPOSE: Landiolol is a beta-blocker used in the treatment of Sepsis. However, how this drug influen...

A Deep Learning-Based EffConvNeXt Model for Automatic Classification of Cystic Bronchiectasis: An Explainable AI Approach.

Cystic bronchiectasis and pneumonia are respiratory conditions that significantly impact morbidity a...

AI in critical care: A roadmap to the future.

Artificial intelligence (AI) has the potential to revolutionize critical care medicine by enhancing ...

A New Method of Modeling the Multi-stage Decision-Making Process of CRT Using Machine Learning with Uncertainty Quantification.

Current machine learning-based (ML) models usually attempt to utilize all available patient data to ...