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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...
| Autores principales: | , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
John Wiley and Sons Inc.
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131786/ http://dx.doi.org/10.1002/advs.202370071 |
| _version_ | 1785031253064417280 |
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| author | Yin, Chenzhong Udrescu, Mihai Gupta, Gaurav Cheng, Mingxi Lihu, Andrei Udrescu, Lucretia Bogdan, Paul Mannino, David M. Mihaicuta, Stefan |
| author_facet | Yin, Chenzhong Udrescu, Mihai Gupta, Gaurav Cheng, Mingxi Lihu, Andrei Udrescu, Lucretia Bogdan, Paul Mannino, David M. Mihaicuta, Stefan |
| author_sort | Yin, Chenzhong |
| collection | PubMed |
| description | 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] |
| format | Online Article Text |
| id | pubmed-10131786 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2023 |
| publisher | John Wiley and Sons Inc. |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-101317862023-04-27 Fractional Dynamics Foster Deep Learning of COPD Stage Prediction (Adv. Sci. 12/2023) Yin, Chenzhong Udrescu, Mihai Gupta, Gaurav Cheng, Mingxi Lihu, Andrei Udrescu, Lucretia Bogdan, Paul Mannino, David M. Mihaicuta, Stefan Adv Sci (Weinh) Frontispiece 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] John Wiley and Sons Inc. 2023-04-26 /pmc/articles/PMC10131786/ http://dx.doi.org/10.1002/advs.202370071 Text en © 2023 Wiley‐VCH GmbH https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
| spellingShingle | Frontispiece Yin, Chenzhong Udrescu, Mihai Gupta, Gaurav Cheng, Mingxi Lihu, Andrei Udrescu, Lucretia Bogdan, Paul Mannino, David M. Mihaicuta, Stefan Fractional Dynamics Foster Deep Learning of COPD Stage Prediction (Adv. Sci. 12/2023) |
| title | Fractional Dynamics Foster Deep Learning of COPD Stage Prediction (Adv. Sci. 12/2023) |
| title_full | Fractional Dynamics Foster Deep Learning of COPD Stage Prediction (Adv. Sci. 12/2023) |
| title_fullStr | Fractional Dynamics Foster Deep Learning of COPD Stage Prediction (Adv. Sci. 12/2023) |
| title_full_unstemmed | Fractional Dynamics Foster Deep Learning of COPD Stage Prediction (Adv. Sci. 12/2023) |
| title_short | Fractional Dynamics Foster Deep Learning of COPD Stage Prediction (Adv. Sci. 12/2023) |
| title_sort | fractional dynamics foster deep learning of copd stage prediction (adv. sci. 12/2023) |
| topic | Frontispiece |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10131786/ http://dx.doi.org/10.1002/advs.202370071 |
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