{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T01:56:54Z","timestamp":1772243814570,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>Numerous diagnostic decisions are made every day by healthcare professionals. Bayesian networks can provide a useful aid to the process, but learning their structure from data generally requires the absence of missing data, a common problem in medical data. We have studied missing data imputation using a step-wise nearest neighbors' algorithm, which we recommended given its limited impact on the assessed validity of structure learning Bayesian network classifiers for Obstructive Sleep Apnea diagnosis.<\/jats:p>","DOI":"10.3233\/978-1-61499-852-5-126","type":"book-chapter","created":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T18:19:26Z","timestamp":1740248366000},"source":"Crossref","is-referenced-by-count":1,"title":["Impact of Imputing Missing Data in Bayesian Network Structure Learning for Obstructive Sleep Apnea Diagnosis"],"prefix":"10.3233","author":[{"family":"Ferreira-Santos Daniela","sequence":"additional","affiliation":[]},{"family":"Monteiro-Soares Matilde","sequence":"additional","affiliation":[]},{"family":"Rodrigues Pedro Pereira","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth"],"original-title":[],"deposited":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T18:25:23Z","timestamp":1740248723000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-851-8&spage=126&doi=10.3233\/978-1-61499-852-5-126"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-852-5-126","relation":{"is-cited-by":[{"id-type":"doi","id":"10.2196\/preprints.25124","asserted-by":"object"},{"id-type":"doi","id":"10.2196\/25124","asserted-by":"object"}]},"ISSN":["0926-9630"],"issn-type":[{"value":"0926-9630","type":"print"}],"subject":[],"published":{"date-parts":[[2018]]}}}