Obstructive Sleep Apnea diagnosis: the Bayesian network model revisited

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Pedro Pereira Rodrigues (Author) FMUP; Daniela Ferreira Santos (Author) Other; Liliana Leite (Author) Other

Conference proceedings International

Title: CBMS

Pages: 115-120

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 4 Citations

Other information

Authenticus ID: P-00G-DT8

DOI: 10.1109/cbms.2015.47

Abstract (EN): Obstructive Sleep Apnea (OSA) is a disease that affects approximately 4% of men and 2% of women worldwide but is still underestimated and underdiagnosed. The standard method for assessing this index, and therefore defining the OSA diagnosis, is polysomnography (PSG). Previous work developed relevant Bayesian network models but those were based only on variables univariatedly associated with the outcome, yielding a bias on the possible knowledge representation of the models. The aim of this work was to develop and validate new Bayesian network decision support models that could be used during sleep consult to assess the need for PSG. Bayesian models were developed using a) expert opinion, b) hill-climbing, c) naive Bayes and d) TAN structures. Resulting models validity was assessed with in-sample AUC and stratified cross-validation, also comparing with previously published model. Overall, models achieved good discriminative power (AUC>70%) and validity (measures consistently above 70%). Main conclusions are a) the need to integrate a wider range of variables in the final models and b) the support of using Bayesian networks in the diagnosis of obstructive sleep apnea.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6