Using weak signals to predict spontaneous breathing trial success: a machine learning approach
We found that ML models are effective in predicting the success of SBT based on biosignals. Predicting weaning from mechanical ventilation thus appears to be a promising area for the application of AI, through the development of multidimensional models to analyze weak signals.
Authors
- Jean Dellamonica (ORCID: https://orcid.org/0000-0002-2681-8260)
- Romain Lombardi (ORCID: https://orcid.org/0000-0003-1101-8153)
- Mathieu Jozwiak (ORCID: https://orcid.org/0000-0003-3379-4065)
- Claude Pasquier (ORCID: https://orcid.org/0000-0001-7498-395X)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Université Côte d'Azur (FR)
- Centre Hospitalier Universitaire de Nice (FR)
- Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis (FR)
- Observatoire de la Côte d’Azur (FR)
- Hôpital Pasteur (FR)
Publication Details
- Journal
- Intensive Care Medicine Experimental
- Published
- 2025-03-18
- DOI
- https://doi.org/10.1186/s40635-025-00724-0
- Citations
- 1
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.35