AIMC Topic: Respiration, Artificial

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SmartAlert: Machine learning-based patient-ventilator asynchrony detection system in intensive care units.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Patient-ventilator asynchronies (PVA) are associated with ventilator-induced lung injury and increased mortality. Current detection methods rely on static thresholds, extensive preprocessing, or proprietary ventilator data. ...

Latest Advances in the Treatment of Patients with Acute Respiratory Distress Syndrome.

Critical care nursing clinics of North America
Acute respiratory distress syndrome (ARDS) is a life-threatening condition characterized by severe inflammation and impaired gas exchange, leading to hypoxemic respiratory failure. It significantly impacts patients by increasing morbidity, mortality,...

Development of a risk prediction model for secondary infection in severe/critical COVID-19 patients.

BMC infectious diseases
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
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
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
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
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
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
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)
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...