AIMC Topic: Respiration, Artificial
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Leveraging large language models for patient-ventilator asynchrony detection.
BMJ health & care informatics
Jun 27, 2025
OBJECTIVES: The objective of this study is to evaluate whether large language models (LLMs) can achieve performance comparable to expert-developed deep neural networks in detecting flow starvation (FS) asynchronies during mechanical ventilation.
Development of a risk prediction model for secondary infection in severe/critical COVID-19 patients.
BMC infectious diseases
May 21, 2025
OBJECTIVE: This study aimed to develop a predictive model for secondary infections in patients with severe or critical COVID-19 by analyzing clinical characteristics and laboratory indicators.
An explainable artificial intelligence framework for weaning outcomes prediction using features from electrical impedance tomography.
Computer methods and programs in biomedicine
Apr 25, 2025
BACKGROUND: Prolonged mechanical ventilation (PMV) might cause ventilator-associated pneumonia and diaphragmatic injury, and may lead to worsening clinical weaning outcomes. The present study proposes a comprehensive machine learning (ML) framework f...
Unraveling relevant cross-waves pattern drifts in patient-hospital risk factors among hospitalized COVID-19 patients using explainable machine learning methods.
BMC infectious diseases
Apr 15, 2025
BACKGROUND: Several studies explored factors related to adverse clinical outcomes among COVID-19 patients but lacked analysis of the impact of the temporal data shifts on the strength of association between different predictors and adverse outcomes. ...
External validation and application of risk prediction model for ventilator-associated pneumonia in ICU patients with mechanical ventilation: A prospective cohort study.
International journal of medical informatics
Apr 4, 2025
BACKGROUND: Early identification and prevention of ventilator-associated pneumonia (VAP) in patients with mechanical ventilation (MV) through reliable prediction model undergoing a rigorous and standardized process is essential for clinical decision-...
Monitoring effort and respiratory drive in patients with acute respiratory failure.
Current opinion in critical care
Apr 4, 2025
PURPOSE OF REVIEW: Accurate monitoring of respiratory drive and inspiratory effort is crucial for optimizing ventilatory support during acute respiratory failure. This review evaluates current and emerging bedside methods for assessing respiratory dr...
Artificial Intelligence for the Detection of Patient-Ventilator Asynchrony.
Respiratory care
Apr 3, 2025
Patient-ventilator asynchrony (PVA) is a challenge to invasive mechanical ventilation characterized by misalignment of ventilatory support and patient respiratory effort. PVA is highly prevalent and associated with adverse clinical outcomes, includin...
Explainable SHAP-XGBoost models for pressure injuries among patients requiring with mechanical ventilation in intensive care unit.
Scientific reports
Mar 22, 2025
pressure injuries are significant concern for ICU patients on mechanical ventilation. Early prediction is crucial for enhancing patient outcomes and reducing healthcare costs. This study aims to develop a predictive model using machine learning techn...
Subphenotyping prone position responders with machine learning.
Critical care (London, England)
Mar 14, 2025
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a heterogeneous condition with varying response to prone positioning. We aimed to identify subphenotypes of ARDS patients undergoing prone positioning using machine learning and assess their a...
Deep learning models for early and accurate diagnosis of ventilator-associated pneumonia in mechanically ventilated neonates.
Computers in biology and medicine
Mar 3, 2025
BACKGROUND: Early and accurate confirmation of critically ill neonates with a suspected diagnosis of ventilator-associated pneumonia (VAP) can optimize the therapeutic strategy and avoid unnecessary use of empirical antibiotics. We aimed to examine w...
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