Area under the expiratory flow-volume curve: predicted values by artificial neural networks
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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2045-2322
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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online resource.
- Bibliographic citation
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Area under the expiratory flow-volume curve: predicted values by artificial neural networks ; volume:10 ; number:1 ; day:6 ; month:10 ; year:2020 ; pages:1-13 ; date:12.2020
Scientific reports ; 10, Heft 1 (6.10.2020), 1-13, 12.2020
- Creator
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Ioachimescu, Octavian C.
Stoller, James K.
Garcia-Rio, Francisco
- Contributor
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SpringerLink (Online service)
- DOI
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10.1038/s41598-020-73925-0
- URN
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urn:nbn:de:101:1-2022101219445065067843
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:22 AM CEST
Data provider
This object is provided by:
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Ioachimescu, Octavian C.
- Stoller, James K.
- Garcia-Rio, Francisco
- SpringerLink (Online service)
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Artificial neural network identification of exercise expiratory flow-limitation in adults
Predicted evaporation in Basrah using artificial neural networks
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Comparison of predicted postoperative forced expiratory volume in the first second (FEV1) using lung perfusion scintigraphy with observed forced expiratory volume in the first second (FEV1) post lung resection
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Accuracy of breeding values of 'unrelated' individuals predicted by dense SNP genotyping
Predicted breeding values for relative scrapie susceptibility for genotyped and ungenotyped sheep
Embedding Values in Artificial Intelligence (AI) Systems
Expected values for the accuracy of predicted breeding values accounting for genetic differences between reference and target populations
Reference value for expiratory time constant calculated from the maximal expiratory flow-volume curve
Area Under the Expiratory Flow–Volume Curve (AEX): Assessing Bronchodilator Responsiveness
Raised-Volume Forced Expiratory Flow-Volume Curve in Healthy Taiwanese Infants
Artificial neural network identification of exercise expiratory flow-limitation in adults
Predicted evaporation in Basrah using artificial neural networks
Artificial Intelligence, Values, and Alignment
Comparison of predicted postoperative forced expiratory volume in the first second (FEV1) using lung perfusion scintigraphy with observed forced expiratory volume in the first second (FEV1) post lung resection
Regional lung densities in alpha-1 antitrypsin deficiency compared to predicted values
Accuracy of breeding values of 'unrelated' individuals predicted by dense SNP genotyping
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Embedding Values in Artificial Intelligence (AI) Systems