Intensivists

WGCNA combined with machine learning identifies histone deacetylation-related diagnostic features for pediatric septic shock.

Pediatric septic shock is a severe form of sepsis with high mortality. Histone deacetylation is involved in sepsis-related disorders. This study investigated the diagnostic value of histone deacetylation-related genes in pediatric septic shock. Three Gene Expression Omnibus datasets (GSE26378, GSE26440, and GSE13904) were analyzed. Differentially expressed genes from GSE26378 and GSE26440 were int...

Multi-Scale Fuzzy Fusion-Based Heterogeneous Granular-Ball Flexible Representation Learning for Multi-View Feature Selection.

Representation learning serves as a critical bridge between human cognition and the data world, constituting an essential component of machine learning architectures where comprehensiveness and flexibility are paramount. However, existing multi-view feature selection methods are constrained by the raw-scale representations, neglecting comprehensive depth-breadth integration and flexible adaptabili...

A Dataset and Benchmarks for Atrial Fibrillation Detection from Electrocardiograms of Intensive Care Unit Patients.

OBJECTIVE: Atrial fibrillation (AF) is the most common cardiac arrhythmia experienced by intensive care unit (ICU) patients and can cause adverse heal...

Wavelet coherence-aware multi-branch deep ensemble for fault identification in centrifugal pumps.

Reliable fault diagnosis of centrifugal pumps is challenging due to the nonstationary nature of vibration signals, weak early-stage laboratory fault s...

Deep learning-based early warning of tailings storage facility instability using Sentinel-1 and Radarsat-2 InSAR calibrated with a geomechanical model.

This study advances the science and application of Interferometric Synthetic Aperture Radar (InSAR) for monitoring tailings storage facilities (TSFs) ...

Machine learning-based prediction of sepsis-induced myocardial injury: external validation and SHAP interpretation.

BACKGROUND: Sepsis-induced myocardial injury (SIMI) is a common complication in sepsis patients with poor prognosis. Consequently, its early accurate ...

Explainable machine learning model for early prediction of ICU death in chronic heart failure with pulmonary infection.

OBJECTIVE: To develop and validate a machine learning model for predicting ICU mortality in CHF patients with pulmonary infection. METHODS: Clinical d...

Heart Failure sub-phenotyping and in-hospital and 28-day mortality prediction based on mean arterial pressure trajectory modeling.

BACKGROUND: Acute decompensated heart failure patients follow a broad range of clinical pathways during hospitalization. Efficient patient sub-phenoty...

MAGMED: A clinically grounded LLM-guided synthetic augmentation framework for admission-time ICU mortality prediction.

BACKGROUND: Mortality risk prediction for elderly intensive care unit (ICU) patients with severe infections remains challenging due to limited sample ...

Reinforcement learning for treatment decision-making in sepsis: a scoping review.

This scoping review summarizes the progress of reinforcement learning (RL) in clinical decision-making for sepsis at the intersection of medicine and ...

Analytical construction of lump, rogue, and multi-wave structures in a nonlinear neuron membrane model via bilinear neural network approach.

In this article, we study a nonlinear neuron membrane model describing the propagation of action potentials along nerve fibers, incorporating nonlinea...

Machine learning on asynchronous clinical pages to predict clinical deterioration.

OBJECTIVES: Clinical deterioration in hospitalized patients is often preventable, but traditional early warning scores based on structured data are li...

Mechanistic study on comorbidities of kidney stones and sepsis using transcriptome sequencing combined with single-cell spatial transcriptome sequencing.

Kidney stones and sepsis have a complex pathological association. Urinary obstruction and infection caused by kidney stones can easily induce sepsis, ...

Development and validation of an interpretable machine learning model for predicting in-hospital mortality among critically ill patients with liver cirrhosis.

This study aimed to develop and validate an interpretable machine learning model for early prediction of in-hospital mortality in critically ill patie...

Using interpretable machine learning to analyze the trajectory changes of serum albumin to predict the mortality rate of sepsis: a cohort study based on MIMIC-IV.

BACKGROUND: Although hypoalbuminemia at ICU admission is associated with increased in-hospital mortality in septic patients, the prognostic value of s...

Deep Model Families for EEG-Based Multi-Class Dementia Classification.

Deep learning (DL) has shown considerable promise for EEG-based dementia assessment; however, rigorous cross-family comparisons under leakage-free and...

Machine Learning-Augmented Traditional Analysis of Lactate vs Lactate-to-Albumin Ratio for Predicting Mortality Risk in Patients With Sepsis: Large-Scale Retrospective Study.

BACKGROUND: Effective risk stratification in sepsis remains a critical clinical challenge. Serum lactate is a cornerstone biomarker of metabolic dysfu...

Artificial intelligence-assisted detection and optical differentiation of colorectal lesions in Lynch syndrome surveillance (CADLY2): a multicentre, open-label, randomised controlled superiority trial.

BACKGROUND: Artificial intelligence (AI)-based computer-aided detection (CADe) systems improve adenoma detection in average-risk colorectal cancer scr...

An informed regression-based knowledge distillation framework for simultaneous prediction of physical and mechanical properties of thermoset epoxy polymers.

Epoxy polymers are widely used due to their multifunctional properties, however their complex 3D molecular structure, multi-component nature, and lack...

Acute kidney injury severity in ICU patients: Developing and evaluating a data-driven analysis of clinical covariates using machine learning.

BACKGROUND: Acute kidney injury remains a major cause of morbidity and mortality in critically ill patients. Existing classification systems rely on s...