AIMC Topic: Patient-Ventilator Asynchrony

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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.
Humans Neural Networks, Computer Language Respiration, Artificial Large Language Models Patient-Ventilator Asynchrony
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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...
Humans Artificial Intelligence Machine Learning Respiration, Artificial Ventilators, Mechanical Patient-Ventilator Asynchrony
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Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method?

Computer methods and programs in biomedicine Jul 11, 2024
BACKGROUND AND OBJECTIVE: Patient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-bas...
Humans Neural Networks, Computer Machine Learning Algorithms Retrospective Studies Female Aged Male Middle Aged Respiration, Artificial Ventilators, Mechanical Patient-Ventilator Asynchrony
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