Critical Care

Multi-relational graph contrastive learning with learnable graph augmentation.

Multi-relational graph learning aims to embed entities and relations in knowledge graphs into low-dimensional representations, which has been successfully applied to various multi-relationship prediction tasks, such as information retrieval, question answering, and etc. Recently, contrastive learning has shown remarkable performance in multi-relational graph learning by data augmentation mechanism...

A machine learning-based electronic nose for detecting neonatal sepsis: Analysis of volatile organic compound biomarkers in fecal samples.

BACKGROUND: Neonatal sepsis is a global health threat, contributing to high morbidity and mortality rates among newborns. Recognizing the profound impact of neonatal sepsis on long-term health outcomes emphasizes the critical need for timely detection to mitigate its consequences and ensure optimal health for the affected newborns. Currently, various diagnostic approaches have been implemented, bu...

Development and validation of a machine-learning model for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage.

Pneumonia is a common postoperative complication in patients with aneurysmal subarachnoid hemorrhage (aSAH), which is associated with poor prognosis a...

Explainable machine learning and online calculators to predict heart failure mortality in intensive care units.

AIMS: This study aims to develop explainable machine learning models and clinical tools for predicting mortality in patients in the intensive care uni...

Extracorporeal Closed-Loop Respiratory Regulation for Patients With Respiratory Difficulty Using a Soft Bionic Robot.

OBJECTIVE: Respiratory regulation is critical for patients with respiratory dysfunction. Clinically used ventilators can lead to long-term dependence ...

PPG2RespNet: a deep learning model for respirational signal synthesis and monitoring from photoplethysmography (PPG) signal.

Breathing conditions affect a wide range of people, including those with respiratory issues like asthma and sleep apnea. Smartwatches with photoplethy...

Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study.

BACKGROUND: Cardiac arrest (CA) is one of the leading causes of death among patients in the intensive care unit (ICU). Although many CA prediction mod...

Ultralow-resistance and self-sterilization biodegradable nanofibrous membranes for efficient PM removal and machine learning-assisted health management.

The development of multifunctional nanofibrous membranes (NFMs) that enable anti-viral protection during air purification and respiratory disease diag...

Criticality of Nursing Care for Patients With Alzheimer's Disease in the ICU: Insights From MIMIC III Dataset.

Alzheimer's disease (AD) patients admitted to intensive care units (ICUs) exhibit varying survival outcomes due to the unique challenges in managing A...

Predicting hypoglycemia in ICU patients: a machine learning approach.

BACKGROUND: The current study sets out to develop and validate a robust machine-learning model utilizing electronic health records (EHR) to forecast t...

Editorial Commentary: The Scope of Medical Research Concerning ChatGPT Remains Limited by Lack of Originality.

There is no shortage of literature surrounding ChatGPT and whether this large language model can provide accurate and clinically relevant information ...

Deep-learning-assisted thermogalvanic hydrogel fiber sensor for self-powered in-nostril respiratory monitoring.

Direct and consistent monitoring of respiratory patterns is crucial for disease prognostication. Although the wired clinical respiratory monitoring ap...

Machine learning predicts acute respiratory failure in pancreatitis patients: A retrospective study.

PURPOSE: The purpose of the research is to design an algorithm to predict the occurrence of acute respiratory failure (ARF) in patients with acute pan...

Integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis.

At present, the diagnosis of lower respiratory tract infections (LRTIs) is difficult, and there is an urgent need for better diagnostic methods. This ...

Prediction of Vascular Access Stenosis by Lightweight Convolutional Neural Network Using Blood Flow Sound Signals.

This research examines the application of non-invasive acoustic analysis for detecting obstructions in vascular access (fistulas) used by kidney dialy...

Development and evaluation of a model for predicting the risk of healthcare-associated infections in patients admitted to intensive care units.

This retrospective study used 10 machine learning algorithms to predict the risk of healthcare-associated infections (HAIs) in patients admitted to in...

CONSTRUCTING A DIAGNOSTIC PREDICTION MODEL TO ESTIMATE THE SEVERE RESPIRATORY SYNCYTIAL VIRUS PNEUMONIA IN CHILDREN BASED ON MACHINE LEARNING.

Background : Severe respiratory syncytial virus (RSV) pneumonia is a leading cause of hospitalization and morbidity in infants and young children. Ear...

Machine learning reveals the rules governing the efficacy of mesenchymal stromal cells in septic preclinical models.

BACKGROUND: Mesenchymal Stromal Cells (MSCs) are the preferred candidates for therapeutics as they possess multi-directional differentiation potential...

Artificial intelligence can regulate light and climate systems to reduce energy use in plant factories and support sustainable food production.

Plant factories with artificial lighting (PFALs) can boost food production per unit area but require resources such as carbon dioxide and energy to ma...

Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3.

BACKGROUND: Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Seps...