Critical Care

An Edge-Cloud-Aided Private High-Order Fuzzy C-Means Clustering Algorithm in Smart Healthcare.

Smart healthcare has emerged to provide healthcare services using data analysis techniques. Especially, clustering is playing an indispensable role in analyzing healthcare records. However, large multi-modal healthcare data imposes great challenges on clustering. Specifically, it is hard for traditional approaches to obtain desirable results for healthcare data clustering since they are not able t...

Deep Learning-Empowered Clinical Big Data Analytics in Healthcare Digital Twins.

With the rapid development of information technology, great changes have taken place in the way of managing, analyzing, and using data in all walks of life. Using deep learning algorithm for data analysis in the field of medicine can improve the accuracy of disease recognition. The purpose is to realize the intelligent medical service mode of sharing medical resources among many people under the d...

Augmenting intensive care unit nursing practice with generative AI: A formative study of diagnostic synergies using simulation-based clinical cases.

BACKGROUND: As generative artificial intelligence (GenAI) tools continue advancing, rigorous evaluations are needed to understand their capabilities r...

Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.

Integrating multi-omics data into predictive models has the potential to enhance accuracy, which is essential for precision medicine. In this study, w...

Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach.

Acute respiratory distress syndrome (ARDS) is a devastating critical care syndrome with significant morbidity and mortality. The objective of this stu...

Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review.

BACKGROUND: With the development of artificial intelligence, the application of machine learning to develop predictive models for sepsis-associated ac...

A machine learning-based predictive model for the in-hospital mortality of critically ill patients with atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is common among intensive care unit (ICU) patients and significantly raises the in-hospital mortality rate. Exist...

Predicting Tracheostomy Need on Admission to the Intensive Care Unit-A Multicenter Machine Learning Analysis.

OBJECTIVE: It is difficult to predict which mechanically ventilated patients will ultimately require a tracheostomy which further predisposes them to ...

The effect of laparoscopic pneumoperitoneum on patient's respiratory variation of inferior vena cava and stroke volume index: A randomized controlled study.

BACKGROUND: The establishment of pneumoperitoneum has impacts on patient's cardiovascular function. In this study, the respiratory variation of inferi...

Evaluating the effectiveness of a sliding window technique in machine learning models for mortality prediction in ICU cardiac arrest patients.

Extensive research has been devoted to predicting ICU mortality, to assist clinical teams managing critical patients. Electronic health records (EHR) ...

Diagnostic Performance of Machine Learning-based Models in Neonatal Sepsis: A Systematic Review.

BACKGROUND: Timely diagnosis of neonatal sepsis is challenging. We aimed to systematically evaluate the diagnostic performance of sophisticated machin...

Adopting machine learning to predict ICU delirium.

With neuropsychiatric complications recognized among COVID-19 patients translating into significant morbidity, we explore the current state-of-the-art...

Deep learning-based respiratory muscle segmentation as a potential imaging biomarker for respiratory function assessment.

Respiratory diseases significantly affect respiratory function, making them a considerable contributor to global mortality. The respiratory muscles pl...

Machine learning for predicting mortality in adult critically ill patients with Sepsis: A systematic review.

INTRODUCTION: Various Machine Learning (ML) models have been used to predict sepsis-associated mortality. We conducted a systematic review to evaluate...

Identification of eupneic breathing using machine learning.

The diaphragm muscle (DIAm) is the primary inspiratory muscle in mammals. In awake animals, considerable heterogeneity in the electromyographic (EMG) ...

Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury.

INTRODUCTION: Sepsis-associated acute kidney injury (SA-AKI) is strongly associated with poor prognosis. We aimed to build a machine learning (ML)-bas...

Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.

Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while la...

Predicting hospital admissions for upper respiratory tract complaints: An artificial neural network approach integrating air pollution and meteorological factors.

This study uses artificial neural networks (ANNs) to examine the intricate relationship between air pollutants, meteorological factors, and respirator...

Machine learning for accurate detection of small airway dysfunction-related respiratory changes: an observational study.

BACKGROUND: The use of machine learning(ML) methods would improve the diagnosis of small airway dysfunction(SAD) in subjects with chronic respiratory ...

Multi-view heterogeneous graph learning with compressed hypergraph neural networks.

Multi-view learning is an emerging field of multi-modal fusion, which involves representing a single instance using multiple heterogeneous features to...