Please use this identifier to cite or link to this item:
|Title:||Monitoring asthma control in children with allergies by soft computing of lung function and exhaled nitric oxide||Authors:||Pifferi, Massimo
Di Cicco, Maria
Boner, Attilio L
|Keywords Plus:||FORCED EXPIRATORY VOLUME;RISK;SEVERITY;CLASSIFICATION;EXACERBATIONS;DIAGNOSIS;SYMPTOMS;EXPOSURE||Mesh headings:||Asthma;Breath Tests;Neural Networks, Computer;Nitric Oxide||Secondary Mesh headings:||Adolescent;Child;Cross-Sectional Studies;Exhalation;Female;Humans;Longitudinal Studies;Male;Predictive Value of Tests;Prospective Studies;Severity of Illness Index;Spirometry;Statistics, Nonparametric||Issue Date:||Feb-2011||Publisher:||ELSEVIER||Journal:||Chest||Abstract:||
Asthma control is emphasized by new guidelines but remains poor in many children. Evaluation of control relies on subjective patient recall and may be overestimated by health-care professionals. This study assessed the value of spirometry and fractional exhaled nitric oxide (FeNO) measurements, used alone or in combination, in models developed by a machine learning approach in the objective classification of asthma control according to Global Initiative for Asthma guidelines and tested the model in a second group of children with asthma.
|Appears in Collections:||Articles|
Show full item record
checked on Jul 26, 2021
checked on Aug 31, 2020
WEB OF SCIENCETM
checked on Dec 2, 2021
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.