Intensivists

Correction: Interpretable machine learning-based real-time sepsis diagnosis.

Integrating host mRNA signatures and clinical trajectories with machine learning for risk stratification and survival prediction in sepsis: an ICU-based prospective cohort study.

BACKGROUND: Sepsis remains a leading cause of death in critically ill patients. Host mRNA biomarkers may provide complementary biological information for prognostic assessment in sepsis. This study aimed to integrate host mRNA biomarkers and clinical parameters to develop and compare predictive models for short-term mortality using both conventional and machine learning (ML) approaches. METHODS: I...

Risk Factor Analysis and Nomogram for Predicting 28-day in-hospital Mortality in ICU Patients with Liver Necrosis.

INTRODUCTION: The mortality risk factors in intensive care unit (ICU) patients with liver necrosis (LN) remain unclear. This study aimed to develop a ...

Predictive modeling of strength characteristics of sustainable concrete using artificial neural networks with luffa fiber and marble dust.

This study involves an integrated approach to predict the mechanical properties of luffa fiber and marble dust- based concrete. It employs Artificial ...

Association between dynamic digital radiography findings and post-extubation respiratory deterioration: A retrospective exploratory analysis of a prospectively collected ICU cohort.

INTRODUCTION: Dynamic Digital Radiography (DDR) is a novel bedside imaging modality that enables real-time visualization of pulmonary motion with mini...

Interpretable machine learning model for predicting kidney failure among CAKUT children in multicenter large-scale study.

Congenital anomalies of the kidney and urinary tract (CAKUT) are the leading cause of pediatric kidney failure, but predicting individual progression ...

Online Sepsis Prediction Using Vital Signs and Multiscale Temporal-Aware Contrastive Learning: Model Development and Validation Study.

BACKGROUND: Real-time prediction of sepsis is a critical yet highly challenging task. Existing studies face 2 major limitations. First, they often rel...

Interpretable Machine Learning for Early Risk Stratification of Carbapenem Resistance Among ICU Patients with Sterile-Site Pseudomonas aeruginosa Isolates: Development and Internal Validation Using MIMIC-IV.

OBJECTIVES: We developed and internally validated an interpretable machine learning model to stratify carbapenem-resistance probability among ICU pati...

A recyclable, mechanically robust, and anti-freezing chitosan/polyvinyl alcohol/polyaniline conductive hydrogel for multifunctional sensing application.

Conductive hydrogels have demonstrated fascinating application potential in flexible electronics. However, conductive hydrogels often exhibit poor mec...

Risk stratification for in-hospital mortality in sepsis-associated acute kidney injury patients receiving continuous renal replacement therapy: an interpretable, externally validated machine learning study.

Patients with sepsis-associated acute kidney injury (SA-AKI) requiring continuous renal replacement therapy (CRRT) have a high risk of in-hospital mor...

Reproducible bicarbonate thresholds predict critically ill patient mortality in an international personalized survival study.

Bicarbonate abnormalities are common in intensive care unit (ICU) patients, but prior mortality studies neglected critical illness pathophysiology. Th...

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 ...

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...

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...

Comparative genomics reveals the evolution of anthraquinone biosynthesis in Polygonaceae.

BACKGROUND: Polygonaceae is a globally distributed angiosperm family rich in anthraquinones (AQs), which contribute to diverse medicinal properties an...

Intelligent soft robotic gripper for non-destructive grasping and attribute recognition via multi-modal waveguide tactile sensors.

The intelligent soft robotic gripper integrated with tactile sensors significantly enhances the robot's execution capabilities in complex tasks, resol...

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...

Mapping Mpox vaccine hesitancy using an integrated machine learning and structural equation modeling approach.

This study employs an integrated computational approach to investigate Mpox vaccine intention in Bangladesh as Mpox immunisation strategies require a ...

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...