Fractional Dynamics Foster Deep Learning of COPD Stage Prediction (Adv. Sci. 12/2023)

Deep Learning Analyzing physiological signals with fractional dynamics reduces the learning complexity for automated diagnosis with deep learning. In article number 2203485, Mihai Udrescu, Paul Bogdan, and co‐workers show that fractional‐order dynamical modeling can extract distinguishing signatures...

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Detalles Bibliográficos
Autores principales: Yin, Chenzhong, Udrescu, Mihai, Gupta, Gaurav, Cheng, Mingxi, Lihu, Andrei, Udrescu, Lucretia, Bogdan, Paul, Mannino, David M., Mihaicuta, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Frontispiece
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131786/
http://dx.doi.org/10.1002/advs.202370071
Descripción
Sumario:Deep Learning Analyzing physiological signals with fractional dynamics reduces the learning complexity for automated diagnosis with deep learning. In article number 2203485, Mihai Udrescu, Paul Bogdan, and co‐workers show that fractional‐order dynamical modeling can extract distinguishing signatures from the physiological signals recorded in COPD patients, then use fractional signatures to develop and train a deep neural network that accurately predicts COPD stages—a robust alternative to spirometry. [Image: see text]

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