A Prognosis Tool Based on Fuzzy Anthropometric and Questionnaire Data for Obstructive Sleep Apnea Severity.
Obstructive sleep apnea (OSA) are linked to the augmented risk of morbidity and mortality. Although polysomnography is considered a well-established method for diagnosing OSA, it suffers the weakness of time consuming and labor intensive, and requires doctors and attending personnel to conduct an ov...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 4; pp. 1 - 13 |
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| Autores principales: | , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2016
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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=115925291&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925291 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Apr2016 vid: 40 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925291 115925291 115925291 10.1007/s10916-016-0464-y 115925291 ppf: 1 ppct: 12 formats: fmt: @attributes: type: P tig: atl: A Prognosis Tool Based on Fuzzy Anthropometric and Questionnaire Data for Obstructive Sleep Apnea Severity. aug: au: Wang, Kung-Jeng Chen, Kun-Huang Huang, Shou-Hung Teng, Nai-Chia affil: Department of Industrial Management, National Taiwan University of Science and Technology, No.43, Sec. 4, Keelung Rd., Da'an Dist. Taipei 106 Republic of China sug: subj: Sleep Apnea, Obstructive Diagnosis Severity of Illness Decision Trees Human Anthropometry Taiwan Female Male Scales Questionnaires Young Adult Adult Middle Age Descriptive Statistics kappa Statistic Confidence Intervals Funding Source Adult: 19-44 years Middle Aged: 45-64 years Female Male ab: Obstructive sleep apnea (OSA) are linked to the augmented risk of morbidity and mortality. Although polysomnography is considered a well-established method for diagnosing OSA, it suffers the weakness of time consuming and labor intensive, and requires doctors and attending personnel to conduct an overnight evaluation in sleep laboratories with dedicated systems. This study aims at proposing an efficient diagnosis approach for OSA on the basis of anthropometric and questionnaire data. The proposed approach integrates fuzzy set theory and decision tree to predict OSA patterns. A total of 3343 subjects who were referred for clinical suspicion of OSA (eventually 2869 confirmed with OSA and 474 otherwise) were collected, and then classified by the degree of severity. According to an assessment of experiment results on g-means, our proposed method outperforms other methods such as linear regression, decision tree, back propagation neural network, support vector machine, and learning vector quantization. The proposed method is highly viable and capable of detecting the severity of OSA. It can assist doctors in pre-diagnosis of OSA before running the formal PSG test, thereby enabling the more effective use of medical resources. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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