Critical Care

Development of an Arterial Carbon Dioxide Estimation Model Using End-Tidal Carbon Dioxide Levels during Surgery in the Pediatric Population.

BACKGROUND: Intraoperative arterial carbon dioxide partial pressure monitoring is essential for pediatric ventilatory management but requires invasive arterial sampling. While end-tidal carbon dioxide offers a noninvasive alternative, its reliability is limited by individual physiological gradients. This study developed a machine learning model to estimate arterial carbon dioxide partial pressure ...

From development to clinical practice: deployment of an interoperable and secure ML-based CDSS to aid in the early detection of sepsis.

Recent research has increasingly focused on machine learning (ML) models for early disease prediction, yet practical frameworks for integrating these models into clinical workflows remain limited. BIAlert is a microservices-based framework designed to operate as a real-time early-warning system for ML-driven disease prediction in hospitalised patients. It can be deployed remotely on physical or vi...

Integrated toxicological analysis links air pollution-associated acute respiratory distress syndrome signatures to benzo[a]pyrene-induced acute lung injury.

Acute respiratory distress syndrome (ARDS) is associated with high mortality, and increasing evidence suggests that air pollution may contribute to it...

Post-Pandemic Forecasting of Pediatric Acute Respiratory Infections with Deep Learning: A Multi-Pathogen, Multi-Time-Horizon Study.

BACKGROUND: Acute respiratory infections (ARIs) remain a major cause of morbidity and hospitalization in children worldwide. In the post-COVID-19 era,...

Artificial intelligence and the future of telemonitoring in home mechanical ventilation.

Artificial intelligence for telemonitoring in home mechanical ventilation https://bit.ly/4q9XxCa.

Deep learning-based natural language processing for critical care identification in pediatric emergency department.

BACKGROUND: The initial assessment of pediatric emergency patients is challenging due to diverse clinical presentations and the limited applicability ...

Implementing Digital Respiratory Technologies for People With Respiratory Conditions: Scoping Review.

BACKGROUND: Digital health offers opportunities for safe, equitable, and accessible care, and its integration into respiratory care is a strategic pri...

Reply - Letter to the editor: Comment on "Explainable Electronic Medical Record-Based Machine Learning to Predict 1-Year Incident Protein-Energy Wasting in Non-Dialysis Chronic Kidney Disease: A Single-Center Development Study".

Aligning neonatal sepsis triage diagnostics with WHO target product profiles in low- and middle-income countries.

Neonatal sepsis is a leading cause of infant morbidity and mortality in low- and middle-income countries (LMICs), yet despite this, progress in the de...

M²B-Net: a lightweight multi-scale multi-attention boundary-aware network for liver tumor segmentation from CT images.

Liver tumor segmentation from CT images remains challenging due to large variations in lesion scale, blurred boundaries, low tissue contrast, and the ...

Prediction of intensive care unit requirement and in-hospital mortality in Fournier's gangrene: a comparative analysis of conventional statistical and machine learning models.

This study aimed to evaluate the need for intensive care unit (ICU) admission and identify factors associated with in-hospital mortality (IHM) in pati...

Advancing Continuous KRT Education: Simulation and Technology-Enhanced Learning in Contemporary Practice.

Continuous renal replacement therapy (CRRT) is a technically complex, high-risk ICU therapy in which preventable interruptions, alarm-related downtime...

Physics-Informed Koopman-Constrained Implicit Q-Learning for Safe Offline Reinforcement Learning in Mechanical Ventilation.

Mechanical ventilation management in intensive care units requires continuous optimization of ventilator parameters while adhering to clinical safety ...

Comment on "Explainable Electronic Medical Record-Based Machine Learning to Predict 1-Year Incident Protein-Energy Wasting in Non-Dialysis Chronic Kidney Disease: A Single-Center Development Study".

A multimodal multi-label deep learning framework with temporal attention for detection of upper-limb motor activities for prosthetic control.

Accurate detection of simultaneous grip and elbow flexion is critical for advanced rehabilitation monitoring, intuitive prosthetic control and human-m...

Plasma proteomic signatures of cellular aging predict human disease.

Aging is asynchronous across cells and organs. Here we tested whether plasma proteomics can be used to analyze cell type-specific aging. From analyses...

Integrated Multi-Omics Analysis and Experimental Validation Identify BCAT1 as a Critical Driver of Hepatocyte Pyroptosis in Acute-On-Chronic Liver Failure.

Acute-on-chronic liver failure (ACLF) is characterized by profound metabolic dysfunction and high mortality. Identifying amino acid metabolism-related...

Predicting major adverse cardiovascular and cerebrovascular events in chronic heart failure: a machine learning study.

BACKGROUND: Heart failure (HF) is a clinical syndrome characterized by impaired cardiac diastolic and systolic function due to structural or functiona...

Zero-Shot Self-Supervised Learning of Single Breath-Hold Magnetic Resonance Cholangiopancreatography (MRCP) Reconstruction.

PURPOSE: To investigate the feasibility of zero-shot self-supervised learning reconstruction for reducing breath-hold times in magnetic resonance chol...

Lactate-Associated Gene Signatures as Predictors: A Comprehensive Analysis of Immune Profiles in Sepsis.

BackgroundSepsis is a complex disorder characterized by a dysregulated immune response to infection. Elevated lactic acid levels and lactylation modif...