Critical Care

Application of interpretable machine learning algorithms to predict acute kidney injury in patients with cerebral infarction in ICU.

BACKGROUND: Acute kidney injury (AKI) is not only a complication but also a serious threat to patients with cerebral infarction (CI). This study aimed to explore the application of interpretable machine learning algorithms in predicting AKI in patients with cerebral infarction.

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.

BACKGROUND: Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management.

Explicate molecular landscape of combined pulmonary fibrosis and emphysema through explainable artificial intelligence: a comprehensive analysis of ILD and COPD interactions using RNA from whole lung homogenates.

Combined pulmonary fibrosis and emphysema (CPFE) presents a unique challenge in respiratory disorders, merging features of interstitial lung disease (...

Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications.

BACKGROUND: Timely detection of modifiable risk factors for postoperative pulmonary complications (PPCs) could inform ventilation strategies that atte...

Multi-modal long document classification based on Hierarchical Prompt and Multi-modal Transformer.

In the realm of long document classification (LDC), previous research has predominantly focused on modeling unimodal texts, overlooking the potential ...

How artificial intelligence could transform emergency care.

Artificial intelligence (AI) in healthcare is the ability of a computer to perform tasks typically associated with clinical care (e.g. medical decisio...

The application of multi-scale simulation in advanced electronic packaging.

Electronic packaging is an essential branch of electronic engineering that aims to protect electronic, microelectronic, and nanoelectronic systems fro...

A machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring.

Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in alarm fatigue. Development of smart algorithms ma...

Entropy-Weighted Numerical Gradient Optimization Spiking Neural System for Biped Robot Control.

The optimization of robot controller parameters is a crucial task for enhancing robot performance, yet it often presents challenges due to the complex...

MTKSVCR: A novel multi-task multi-class support vector machine with safe acceleration rule.

Regularized multi-task learning (RMTL) has shown good performance in tackling multi-task binary problems. Although RMTL can be used to handle multi-cl...

An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection.

BACKGROUND AND OBJECTIVE: Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchro...

Towards quantifying biomarkers for respiratory distress in preterm infants: Machine learning on mid infrared spectroscopy of lipid mixtures.

Neonatal respiratory distress syndrome (nRDS) is a challenging condition to diagnose which can lead to delays in receiving appropriate treatment. Mid ...

Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission.

BACKGROUND AND OBJECTIVE: Critically ill children may suffer from impaired neurocognitive functions years after ICU (intensive care unit) discharge. T...

Machine learning in the prediction and detection of new-onset atrial fibrillation in ICU: a systematic review.

Atrial fibrillation (AF) stands as the predominant arrhythmia observed in ICU patients. Nevertheless, the absence of a swift and precise method for pr...

Improving respiratory signal prediction with a deep neural network and simple changes to the input and output data format.

To improve respiratory gating accuracy and radiation treatment throughput, we developed a generalized model based on a deep neural network (DNN) for p...

The value of artificial intelligence for the treatment of mechanically ventilated intensive care unit patients: An early health technology assessment.

PURPOSE: The health and economic consequences of artificial intelligence (AI) systems for mechanically ventilated intensive care unit patients often r...

Predicting Extubation Readiness in Preterm Infants Utilizing Machine Learning: A Diagnostic Utility Study.

OBJECTIVE: The objective of this study was to predict extubation readiness in preterm infants using machine learning analysis of bedside pulse oximete...

Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks.

OBJECTIVE: This study aims to design, validate and assess the accuracy a deep learning model capable of differentiation Chest X-Rays between pneumonia...

Two-step interpretable modeling of ICU-AIs.

We present a novel methodology for integrating high resolution longitudinal data with the dynamic prediction capabilities of survival models. The aim ...

Machine learning in risk prediction of continuous renal replacement therapy after coronary artery bypass grafting surgery in patients.

OBJECTIVES: This study aimed to develop machine learning models for risk prediction of continuous renal replacement therapy (CRRT) following coronary ...