Intensivists

Development and Internal Validation of a Gradient Boosting Model for Pressure Injury Risk in the ICU.

Pressure injury (PI) is common in the ICU and not well captured by single-risk tools such as the Braden scale. We aimed to develop and internally validate a machine-learning model to predict new-onset PI using routinely collected ICU data. This retrospective single-centre cohort included adult ICU patients with length of stay ≥ 48 h (2018-2023). The primary outcome was new-onset PI during ICU stay...

Artificial intelligence in intensive care units: a scoping review addressing the translational gap to clinical practice.

BACKGROUND: Critically ill patients generate large volumes of complex data, creating challenges for timely clinical decision making in intensive care units (ICUs). Artificial intelligence (AI) has emerged as a promising tool for supporting diagnosis, monitoring, prognostication, and workflow optimization in this setting. This scoping review aimed to map current AI applications in critical care and...

Conventional and novel platelet parameters: Clinical and laboratory approaches.

INTRODUCTION: Platelet count and related indices obtained from the complete blood count (CBC) play an important role in the diagnosis, monitoring, and...

Predictive modeling and experimental validation of mechanical-microstructural relationships in 3D-printed Onyx-fibre composites.

Additive Manufacturing (AM) has emerged as a revolutionary fabrication technology for producing high-performance polymer-based composite materials. Fu...

Towards efficient language giants: A comprehensive survey on structural optimizations and compression techniques for large language models.

The impressive success of large language models (LLMs) across a broad spectrum of NLP tasks has attracted considerable attention in both academia and ...

Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence.

BACKGROUND: Delayed or missed diagnosis of congenital heart disease (CHD) contributes to excess pediatric mortality worldwide. Echocardiography (echo)...

Establishment and validation of a machine learning-based prediction model for sepsis-induced coagulopathy.

Machine learning-driven design of multiscale reinforced horse chestnut starch-based composite films for biodegradable packaging applications.

To address the poor mechanical strength, limited thermal stability, and inadequate UV-shielding capability of starch films, as well as to overcome the...

Proteomic profiling and pathway analyses reveal molecular signatures and immune networks in pediatric sepsis.

BACKGROUND: Sepsis remains a leading cause of childhood mortality worldwide. Most deaths occur within the first few days of presentation, underscoring...

Integrating metabolomics and machine learning with in silico analysis to identify early biomarkers and molecular interactions in sepsis-associated acute kidney injury.

AI-derived left-to-right cardiac chamber volume ratios in coronary artery calcium scans strongly predict heart failure.

AIMS: The AI-CVD initiative seeks to extract actionable insights from coronary artery calcium (CAC) scans beyond the traditional CAC score. We previou...

At the Edge of Chaos: Complexity Science and Critical Care in the Era of Artificial Intelligence.

Critical care unfolds at the crossroads of highly complex, nonlinear systems. The intersection of pathophysiology, human behavior, and clinical operat...

A comprehensive analysis of brain network complexity in task-based fMRI using entropy: systematic review.

Entropy-based analysis is increasingly used in task-based functional magnetic resonance imaging (fMRI) to quantify neural signal complexity and inform...

Machine learning predicts sepsis deterioration trajectories.

Sepsis has heterogeneous clinical trajectories, but conventional severity scores offer only static risk estimates. Timely, dynamic prediction could en...

Subtypes estimation in single-cell multi-omics data via S-multi-SNE.

Single-cell RNA sequencing (scRNA-seq) has rapidly become an essential method in modern biology, as it allows global characterisation of transcriptomi...

Nonlinear dynamical analysis of chaos, fractal complexity, and entropy in EEG signals during music therapy in ICU burn patients.

Music therapy (MT) is known to influence brain dynamics; however, its effects on nonlinear electroencephalogram (EEG) characteristics in clinical sett...

International Delphi consensus on acute kidney injury: Foundations for AI-driven digital twin development in critical care nephrology.

BACKGROUND: Acute kidney injury (AKI) in critically ill patients is clinically complex and heterogeneous, limiting the development of structured simul...

Enhanced mechanical and shielding properties of heavy concrete: A machine learning approach to mix proportion optimization.

Shielding concrete is a multifunctional material that combines structural support with radiation shielding, offering significant practical value. Howe...

A cost-sensitive multiclass machine learning framework for postoperative neurosurgical triage (Neuro-TACTIC).

Postoperative placement of patients into a regular ward, an intermediate-care unit (IMC), or an intensive care unit (ICU) is critical for balancing pa...

Integrated analysis of the gut mycobiota and plasma metabolome reveals mortality and age-specific characteristics in sepsis.