Automatic classification of patient-ventilator asynchronies with a 1D convolutional neural network: a pilot study - Vienna 2024

New in respiratory intensive care medicine

Chair(s): Christoph Fisser, Ana Cysneiros

Session type: Oral presentation

Automatic classification of patient-ventilator asynchronies with a 1D convolutional neural network: a pilot study

Alessandro Molani

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Late Breaking Abstract - Pulmonary CMV and HSV reactivations in critically ill COVID-19 patients with and without aspergillosis

Jannes Heylen

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A ML and DL models to predict the Readmission or Death after Discharge from ICU

Da Hye Moon

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Impact of empirical treatment recommendations of European guidelines for nosocomial pneumonia. A prospective cohort study

Davide Calabretta

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Survey on Bronchoscopy in Intensive Care Unit

Christoph Fisser

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Diagnostic accuracy of Early ulTrasound measurement coupled with neuromuscular EleCTrical stimulation for diagnosing ICU-acquired weakness: an observational study (DETECT)

Margaux Machefert

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Hospital prognosis of invasive pulmonary aspergillosis patients admitted to intensive care unit and one-year outcomes in hospital survivors:A retrospective longitudinal study

Meiyuan Li

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Impact of ventilatory support modalities and timing of intubation on longitudinal recovery of pulmonary sequelae in critically ill Covid-19 patients

Jessica Gonzalez Gutierrez

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Automatic classification of patient-ventilator asynchronies with a 1D convolutional neural network: a pilot study

Alessandro Molani

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Late Breaking Abstract - Abnormal breathing patterns limit exercise in mechanically ventilated patients: an observational study

Thomas Nijhoff

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Discussion and Q&A

CDN Connection Issue

The ERS Congress 2024, Vienna

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