Area under the expiratory flow-volume curve: predicted values by artificial neural networks.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
06 10 2020
06 10 2020
Historique:
received:
06
04
2020
accepted:
23
09
2020
entrez:
7
10
2020
pubmed:
8
10
2020
medline:
8
10
2020
Statut:
epublish
Résumé
Area under expiratory flow-volume curve (AEX) has been proposed recently to be a useful spirometric tool for assessing ventilatory patterns and impairment severity. We derive here normative reference values for AEX, based on age, gender, race, height and weight, and by using artificial neural network (ANN) algorithms. We analyzed 3567 normal spirometry tests with available AEX values, performed on subjects from two countries (United States and Spain). Regular linear or optimized regression and ANN models were built using traditional predictors of lung function. The ANN-based models outperformed the de novo regression-based equations for AEX
Identifiants
pubmed: 33024243
doi: 10.1038/s41598-020-73925-0
pii: 10.1038/s41598-020-73925-0
pmc: PMC7538954
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
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