Machine learning‑based identification of efficient and restrictive physiological subphenotypes in acute respiratory distress syndrome

Meza‑Fuentes, Gabriela; Delgado, Iris; Barbé, Mario; Sánchez‑Barraza, Ignacio; Retamal, Mauricio; López, René

Publication:
Machine learning‑based identification of efficient and restrictive physiological subphenotypes in acute respiratory distress syndrome

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s40635-025-00737-9.pdf (1.47 MB)

Date

2025

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Article

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Artículos Medicina y Ciencias de la Salud

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1

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Authors

Meza‑Fuentes, Gabriela

Delgado, Iris

Barbé, Mario

Sánchez‑Barraza, Ignacio

Retamal, Mauricio

López, René

item.page.doiurl

https://doi.org/10.1186/s40635-025-00737-9

URI

https://hdl.handle.net/11447/11041

item.page.extent

11 p.

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Acceso abierto

Abstract

Introduction Acute respiratory distress syndrome (ARDS) is a severe condition with high morbidity and mortality, characterized by significant clinical heterogeneity. This heterogeneity complicates tre...

Citation

Meza-Fuentes, G., Delgado, I., Barbé, M. et al. Machine learning-based identification of efficient and restrictive physiological subphenotypes in acute respiratory distress syndrome. ICMx 13, 29 (2025). https://doi.org/10.1186/s40635-025-00737-9

Keywords

Acute respiratory distress syndrome , Subphenotypes , Artificial intelligence , Personalized medicine

item.page.dc.rights

Atribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)

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http://creativecommons.org/licenses/by-nc-sa/3.0/cl/

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