Critical Care

Phenotype discovery and mortality prediction in sepsis-induced myocardial dysfunction: a deep learning and stratified modeling approach.

Sepsis-induced myocardial dysfunction (SIMD) is a common and heterogeneous complication in patients with sepsis and is associated with increased mortality. This study aimed to identify distinct clinical phenotypes of SIMD using unsupervised deep learning and to develop an optimal short-term survival prediction model based on phenotypic stratification. Data from SIMD patients in the MIMIC-III and M...

WaveMamba-Net: Dual-frequency adaptive wavelet state-space network for real-time pulse signal classification in wearable health monitoring.

Continuous cardiovascular monitoring via wearable devices is critical for early disease detection, yet existing pulse signal analysis methods struggle to achieve both high accuracy and real-time performance under noisy, imbalanced conditions. We propose WaveMamba-Net, a deep learning framework integrating wavelet-based multi-scale decomposition with state-space modeling for patient status classifi...

Interpretable machine learning to predict postoperative adverse outcomes in cardiac surgery.

BACKGROUND: Cardiac surgery is associated with significant mortality and complication risks. This study aims to develop an interpretable machine learn...

Nomogram and machine learning models for predicting the risk of delirium in ICU patients with NSTEMI.

Delirium is a frequent and clinically consequential complication among patients admitted to the intensive care unit (ICU). Early risk stratification i...

Application of machine learning to predict bronchiolitis severity in children: a single-centre retrospective cohort study.

UNLABELLED: Several severity scores have been developed to assess disease severity in infants with bronchiolitis, but they often lack objectivity and ...

Transcriptome and experimental verification identified candidate biomarkers related to mitochondrial metabolism in sepsis-associated encephalopathy.

BACKGROUND: Mitochondrial metabolism (MM) abnormalities have been implicated in multiple diseases, but its contribution to sepsis-associated encephalo...

Effect of a Personalized Mobile Health Intervention Using Artificial Intelligence (the WARIFA App) Versus a Nonpersonalized Intervention on User-Defined Objectives, Healthy Lifestyles, and Management of Type 1 Diabetes (T1D): Protocol for a Randomized Controlled Trial.

BACKGROUND: Noncommunicable diseases are the leading cause of death worldwide. Cardiovascular and respiratory diseases, cancer, and type 2 diabetes sh...

An explainable machine learning model for early pediatric sepsis prediction using cytokine and routine laboratory data.

BACKGROUND: Early sepsis diagnosis in children remains challenging due to nonspecific presentations. This study aimed to develop an interpretable mach...

Statistical Models Addressing Acute Respiratory Diseases: A Scoping Review.

This review highlights the application of statistical models in addressing respiratory diseases worldwide. A scoping review was conducted following Jo...

Integrating Causal Inference and Agent-Based Modelling to Assess the Impact of Clinicians' Guideline Adherence in Older Adults Hospitalized with Pneumonia.

This study integrates agent-based modeling (ABM) and causal machine learning (ML) to assess the impact of clinicians' adherence to antibiotic guidelin...

Predicting Pediatric Mortality Across Five Intensive Care Units: Toward an Early Warning Using Machine Learning.

Despite the advances in critical care and innovations of medical technology, earlier identification of children at high mortality risk remains challen...

Assessing AI-Based Decision Support in Early Sepsis and AKI Recognition.

Sepsis and acute kidney injury (AKI) remain among the most critical conditions in acute care, associated with high morbidity and mortality. Early risk...

Diagnostic Accuracy of Pressure Injury Risk Assessment Tools for Critically Ill Patients: A Systematic Review and Network Meta-Analysis.

AIMS AND OBJECTIVES: To systematically evaluate and compare the diagnostic accuracy of pressure injury risk assessment tools in critically ill adult p...

Artificial Intelligence Algorithm to Monitor Inspiratory Muscle Effort and Patient-Ventilator Dyssynchrony During Mechanical Ventilation.

OBJECTIVE: Current methods for estimating inspiratory muscle pressure (Pmus) during mechanical ventilation are either invasive or dependent on occlusi...

Integrating network pharmacology, molecular docking, machine learning, and experimental validation: puerarin improves sepsis-induced acute kidney injury via the Sirt1-Nrf2-HO-1 pathway.

Acute kidney injury (AKI) associated with sepsis has a high clinical mortality rate, and there is a lack of effective therapeutic targets; uncontrolle...

Decoding shared pathogenic networks of oxidative stress in neuropsychiatric disorders to prioritize multi-target therapeutics from natural products.

BACKGROUND: Neurodegenerative and psychiatric disorders, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and ...

ICU command centers and evidence-based artificial intelligence - Letter on Munoz et al.

Enhancing respiratory virus surveillance among hospitalised children: a machine learning-based predictive model.

BACKGROUND: Viral respiratory tract infections (vRTIs) are a leading cause of paediatric hospitalisation and healthcare utilisation. Existing syndromi...

[Automated analysis of mandibular movements for the screening of obstructive sleep apnea].

INTRODUCTION: Obstructive sleep apnea syndrome (OSAS) is a highly prevalent condition, particularly among at-risk populations such as patients with ob...

Machine learning modeling of vegetation and limited two dimensional urban morphology effects on land surface temperature in Osaka using open data.

Urban heat islands (UHI) significantly elevate land surface temperatures (LST) in high-density subtropical cities like Osaka, Japan, exacerbating ener...