Area under the expiratory flow-volume curve: predicted values by artificial neural networks.

ABSTRACT: 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_predicted and AEX z scores using race, gender, age, height and weight as predictor factors. We compared these reference values with previousl

SUBMITTER: Ioachimescu OC 

PROVIDER: S-EPMC7538954 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature