Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a very serious progressive lung disease with a high socioeconomic impact and prevalence levels worldwide. Admissions for acute exacerbation of respiratory symptoms (AECOPD) have the highest proportion of economic and human cost. During a 6-months field trial in a group of 16 patients, a novel electronic questionnaire for the early detection of COPD exacerbations was evaluated. Data mining strategies were applied. A Radial Basis Function (RBF) network classifier was trained and validated and its accuracy in detecting AECOPD was assessed. 94% (31 out of 33) AECOPD were early detected. Sensitivity and specificity were 73.8% and 87.0% respectively and area under the ROC curve was 0.82. The system was able to early detect AECOPD with 5.3 ± 2.1 days prior to the day in which the patients required medical attention.
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© 2014 Springer International Publishing Switzerland
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Granero, M.A.F., Morillo, D.S., León, A., Gordo, M.A.L., Crespo, L.F. (2014). Radial-Basis-Function Based Prediction of COPD Exacerbations. In: Roa Romero, L. (eds) XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013. IFMBE Proceedings, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-00846-2_360
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DOI: https://doi.org/10.1007/978-3-319-00846-2_360
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00845-5
Online ISBN: 978-3-319-00846-2
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