Critical Care

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 health effects. In this study, we publish a labelled ICU dataset and bench marks for AF detection. METHODS: We compared machine learning models across three data-driven artificial intelligence (AI) approaches: feature-based classifiers, deep learning (DL...

A novel fever prevalence indicator for early warning of acute respiratory infections using wastewater-based epidemiology and machine learning.

The rapid spread of acute respiratory infections (ARIs) poses a major challenge to global public health, yet current surveillance systems relying on s...

Deep learning for multimodal physiological signal based assessment of sleep disordered breathing.

Sleep disordered breathing (SDB) is commonly assessed using polysomnography (PSG), which records multiple physiological signals during sleep. Accurate...

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) ...

Domain-independent PPG signal quality assessment framework via representation learning.

Photoplethysmography (PPG) is a widely used non-invasive sensing technique for monitoring cardiovascular and respiratory parameters, however, its meas...

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...

Predictive modeling of fluid status in hemodialysis: model development and internal validation using the MONitoring dialysis outcomes (MONDO) global database.

BACKGROUND: Optimized fluid management is crucial in dialysis care because extracellular volume overload drives adverse cardiovascular outcomes. At th...

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 ...

Bridging the Vaccine Gap: Scientific and Technological Advances for Diseases Lacking Effective Vaccines.

Despite remarkable achievements in infectious disease control, more than 20 major pathogens responsible for significant global morbidity and mortality...

Association of Histological Features of Cortical and Medullary Peritubular Capillaries With Progressive CKD.

RATIONALE & OBJECTIVE: Peritubular capillary (PTC) rarefaction occurs in chronic kidney disease (CKD), but the independent association of PTC histolog...

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 ...

Exploratory development of prediction models for pharmacotherapy outcomes in trigeminal neuralgia: a combined analysis based on multi-source data.

BACKGROUND: Drug-refractory trigeminal neuralgia (DRTN) represents a formidable challenge in clinical management, with approximately 30%-50% of patien...

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, ...

Anchor-free temporal localization of apnea events from EEG/EOG with state-space models.

Reduced-channel polysomnography (PSG) and electroencephalography/electrooculography (EEG/EOG) signals can support obstructive sleep apnea (OSA) screen...

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...

Prediction of bronchopulmonary dysplasia seven days after birth using respiratory and oxygenation timeseries with machine learning.

BACKGROUND: Accurate prediction of bronchopulmonary dysplasia (BPD) development would allow targeted early treatment. This study aims to develop a mac...

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...

Understanding HFpEF Through Imaging: Strengths, Limitations, and Current State of the Art.

Heart failure with preserved ejection fraction (HFpEF) is a clinical syndrome characterized by dyspnea caused by hemodynamic congestion, which develop...