Machine learning-based identification of efficient and restrictive physiological subphenotypes in acute respiratory distress syndrome
This study presents an approach to improve subphenotype categorization in ARDS. The generation of clustering and prediction models by machine learning involving clinical, ventilatory mechanics, and gas exchange variables allowed for more accurate stratification of patients. These findings have the potential to optimize individualized treatment selection and improve clinical outcomes in patients with ARDS.
Authors
- Mauricio A. Retamal (ORCID: https://orcid.org/0000-0003-2562-6686)
- René López (ORCID: https://orcid.org/0000-0003-4026-6367)
- Iris Delgado (ORCID: https://orcid.org/0000-0002-7021-3357)
- Mario Barbé (ORCID: https://orcid.org/0000-0002-9755-803X)
- Gabriela Meza-Fuentes (ORCID: https://orcid.org/0000-0003-4645-078X)
- Ignacio Sánchez-Barraza
Institutions
- Universidad del Desarrollo (CL)
- Clínica Alemana (CL)
Publication Details
- Journal
- Intensive Care Medicine Experimental
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1186/s40635-025-00737-9
- Citations
- 7
- Primary Topic
- Respiratory Support and Mechanisms
- Type
- article
- Field-Weighted Citation Impact
- 10.63