AIMC Topic: Respiration, Artificial
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Predicting ICU Interventions: A Transparent Decision Support Model Based on Multivariate Time Series Graph Convolutional Neural Network.
IEEE journal of biomedical and health informatics
Jun 6, 2024
In this study, we present a novel approach for predicting interventions for patients in the intensive care unit using a multivariate time series graph convolutional neural network. Our method addresses two critical challenges: the need for timely and...
Artificial intelligence as a further step in the detection of dyspnea in the critically ill mechanically ventilated patient.
Intensive care medicine
May 2, 2024
An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection.
Computer methods and programs in biomedicine
Apr 12, 2024
BACKGROUND AND OBJECTIVE: Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While t...
The value of artificial intelligence for the treatment of mechanically ventilated intensive care unit patients: An early health technology assessment.
Journal of critical care
Apr 6, 2024
PURPOSE: The health and economic consequences of artificial intelligence (AI) systems for mechanically ventilated intensive care unit patients often remain unstudied. Early health technology assessments (HTA) can examine the potential impact of AI sy...
Development of a machine learning model for prediction of the duration of unassisted spontaneous breathing in patients during prolonged weaning from mechanical ventilation.
Journal of critical care
Mar 26, 2024
PURPOSE: Treatment of patients undergoing prolonged weaning from mechanical ventilation includes repeated spontaneous breathing trials (SBTs) without respiratory support, whose duration must be balanced critically to prevent over- and underload of re...
Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques.
Critical care (London, England)
Mar 14, 2024
BACKGROUND: Flow starvation is a type of patient-ventilator asynchrony that occurs when gas delivery does not fully meet the patients' ventilatory demand due to an insufficient airflow and/or a high inspiratory effort, and it is usually identified by...
Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery.
British journal of anaesthesia
Feb 26, 2024
BACKGROUND: Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, def...
Detection and quantitative analysis of patient-ventilator interactions in ventilated infants by deep learning networks.
Pediatric research
Feb 5, 2024
BACKGROUND: The study of patient-ventilator interactions (PVI) in mechanically ventilated neonates is limited by the lack of unified PVI definitions and tools to perform large scale analyses.
Artificial intelligence in the NICU to predict extubation success in prematurely born infants.
Journal of perinatal medicine
Dec 8, 2023
OBJECTIVES: Mechanical ventilation in prematurely born infants, particularly if prolonged, can cause long term complications including bronchopulmonary dysplasia. Timely extubation then is essential, yet predicting its success remains challenging. Ar...
Natural language processing diagnosed behavioural disturbance phenotypes in the intensive care unit: characteristics, prevalence, trajectory, treatment, and outcomes.
Critical care (London, England)
Nov 4, 2023
BACKGROUND: Natural language processing (NLP) may help evaluate the characteristics, prevalence, trajectory, treatment, and outcomes of behavioural disturbance phenotypes in critically ill patients.
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