Critical Care

Development and internal validation of machine learning in predicting prognosis of acute kidney injury patients in resource-limited setting.

BACKGROUND: Machine learning models for predicting acute kidney injury (AKI) prognosis have primarily been developed in resource-rich settings, with limited validation in resource-limited environments. This study applied machine learning techniques to predict in-hospital mortality and major adverse kidney events within 28 days (MAKE-28) among critically ill patients with AKI in Southeast Asia. MET...

Development of a rapid metal oxide semiconductor-based sensory system for noninvasive neonatal sepsis detection.

Systemic immune-inflammation (SII) index as a novel prognostic biomarker in critically ill patients with sepsis: analysis of the MIMIC-IV cohort and predictive modeling based on machine learning.

Application of machine learning for the diagnosis and prognosis of sepsis-induced acute respiratory distress syndrome: a systematic review and meta-analysis.

A cascaded CNN-LSTM framework for quantifying respiratory motion from surface electromyographic signals.

OBJECTIVE: Surface electromyographic (sEMG) signals of the diaphragm provide a valuable physiological signal for real-time respiratory monitoring, par...

A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet.

Electrocardiography is a cornerstone in the diagnosis of cardiovascular diseases; however, accurate interpretation demands expert knowledge and is oft...

Flow-driven left ventricular remodelling in long-term haemodialysis: a multicenter model of arteriovenous access-induced cardiac load.

BACKGROUND: Arteriovenous (AV) access, an essential component of dialysis treatment, has been implicated in the development of cardiovascular abnormal...

When distress meets technology: The mediating role of AI integration in the link between nurses' moral distress and moral integrity in critical care settings.

BACKGROUND: Moral distress is increasingly recognized as a critical challenge in critical care settings, where nurses frequently encounter ethically c...

Using human-robot interaction to assess emotion recognition in dialysis patients: A preliminary study.

This cross-sectional observational study examined the relationship between psychological factors and multimodal emotion recognition abilities in patie...

The effect of artificial intelligence-assisted pulmonary rehabilitation on exercise capacity: A systematic review and meta-analysis.

INTRODUCTION: Artificial intelligence (AI) technologies are increasingly being integrated into pulmonary rehabilitation (PR) to improve individualizat...

Synergistic impact of Gram-negative bacterial co-infection on invasive pulmonary aspergillosis in non-neutropenic patients: a machine learning-based risk stratification approach.

BACKGROUND: Invasive pulmonary aspergillosis (IPA) is increasingly recognized in non-neutropenic patients, where coexisting bacterial infections, part...

A randomized controlled trial of artificial intelligence-based analytics for clinical deterioration.

This pragmatic randomized controlled trial aimed to assess the effect of a passive display of artificial intelligence (AI)-based predictive analytics ...

Explainable machine learning-based 28-day mortality prediction model for elderly patients with acute kidney injury.

Elderly patients with acute kidney injury (AKI) face a significantly increased mortality risk. Recent advances in machine learning technology have mad...

Evaluation of oxygenation indices incorporating SpO₂ and PEEP for assessing ARDS severity: Evidence from the MIMIC-IV and eICU collaborative research database v2.0 databases.

BACKGROUND: The in-hospital mortality of acute respiratory distress syndrome can reach 35-45%, with patients requiring a more convenient and accurate ...

Iatrogenic plasticizer Di(2-ethylhexyl) phthalate (DEHP) exposure increases Sepsis mortality risk: Machine learning implicates monocyte-driven immune dysregulation.

Sepsis poses a significant global health burden, and ICU patients are disproportionately exposed to di(2-ethylhexyl) phthalate (DEHP), an immunotoxic ...

Impacts of delayed influenza vaccination on clinical outcomes in ICU-admitted patients with influenza: A retrospective cohort study.

BACKGROUND: Influenza vaccination is an effective measure for reducing the risk of severe influenza infection. However, the task of achieving adequate...

A scoping review of portable ultra-low-field MRI studies in patients with acquired brain injury: Past, present, and future.

Acquired Brain Injury (ABI) refers to any post-birth damage to the brain, commonly resulting from traumatic events (traumatic brain injury) or non-tra...

3DFE-Net: Three-dimensional fusion enhancement network based on multi-attention mechanism for multi-modal magnetic resonance images.

Nowadays, the research of image fusion methods focuses on two-dimensional medical images, and almost no three-dimensional medical image fusion methods...

Explainable AI reveals temporal risk pathways in fall prediction: Extracting clinical insights from multi-horizon machine learning models.

Falls are a leading cause of injury in older adults, making risk prediction a clinical priority. While many machine learning (ML) models exist, they t...

Explainable machine learning model for predicting short-term outcomes in sepsis- induced coagulopathy.

BACKGROUND: Sepsis-Induced coagulopathy (SIC) is not only a common complication in the development process of sepsis but also related to poor prognosi...