A novel approach to prediction of mild obstructive sleep disordered breathing in a population-based sample: the sleep heart health study.
This manuscript considers a data-mining approach for the prediction of mild obstructive sleep disordered breathing, defined as an elevated respiratory disturbance index (RDI), in 5,530 participants in a community-based study, the Sleep Heart Health Study. The prediction algorithm was built using mod...
| Publicado en: | Sleep Vol. 33; no. 12; pp. 1641 - 1649 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
2010 Dec 1
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104955885&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104955885 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01618105 2EI jtl: Sleep issn: 01618105 maglogo: N pubinfo: dt: 2010 Dec 1 vid: 33 iid: 12 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104955885 104955885 2010873696 NLM21120126 PMC2982734 104955885 ppf: 1641 ppct: 8 formats: tig: atl: A novel approach to prediction of mild obstructive sleep disordered breathing in a population-based sample: the sleep heart health study. aug: au: Caffo B Diener-West M Punjabi NM Samet J sug: subj: Sleep Apnea Syndromes Diagnosis Sleep Apnea Syndromes Epidemiology Age Factors Aged Boosting Machine Learning Algorithms Body Constitution Body Mass Index Clinical Assessment Tools Female Human Male Middle Age Predictive Value of Tests Prospective Studies Risk Factors ROC Curve Snoring Complications Aged: 65+ years Middle Aged: 45-64 years Female Male ab: This manuscript considers a data-mining approach for the prediction of mild obstructive sleep disordered breathing, defined as an elevated respiratory disturbance index (RDI), in 5,530 participants in a community-based study, the Sleep Heart Health Study. The prediction algorithm was built using modern ensemble learning algorithms, boosting in specific, which allowed for assessing potential high-dimensional interactions between predictor variables or classifiers. To evaluate the performance of the algorithm, the data were split into training and validation sets for varying thresholds for predicting the probability of a high RDI (>= 7 events per hour in the given results). Based on a moderate classification threshold from the boosting algorithm, the estimated post-test odds of a high RDI were 2.20 times higher than the pre-test odds given a positive test, while the corresponding post-test odds were decreased by 52% given a negative test (sensitivity and specificity of 0.66 and 0.70, respectively). In rank order, the following variables had the largest impact on prediction performance: neck circumference, body mass index, age, snoring frequency, waist circumference, and snoring loudness. CITATION: Caffo B; Diener-West M; Punjabi NM; Samet J. A novel approach to prediction of mild obstructive sleep disordered breathing in a population-based sample: the Sleep Heart Health Study. SLEEP 2010;33(12):1641-1648. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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