Supervised Machine Learning Applied to Automate Flash and Prolonged Capillary Refill Detection by Pulse Oximetry

OBJECTIVE: Develop an automated approach to detect flash (<1.0 s) or prolonged (>2.0 s) capillary refill time (CRT) that correlates with clinician judgment by applying several supervised machine learning (ML) techniques to pulse oximeter plethysmography data. MATERIALS AND METHODS: Data was co...

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Detalles Bibliográficos
Autores principales: Hunter, Ryan Brandon, Jiang, Shen, Nishisaki, Akira, Nickel, Amanda J., Napolitano, Natalie, Shinozaki, Koichiro, Li, Timmy, Saeki, Kota, Becker, Lance B., Nadkarni, Vinay M., Masino, Aaron J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Physiology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7574820/
https://www.ncbi.nlm.nih.gov/pubmed/33117190
http://dx.doi.org/10.3389/fphys.2020.564589

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7574820/
https://www.ncbi.nlm.nih.gov/pubmed/33117190
http://dx.doi.org/10.3389/fphys.2020.564589

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