Cited 0 times in
Prediction of difficult intubation using a neural network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김원옥 | - |
dc.date.accessioned | 2019-11-11T05:07:10Z | - |
dc.date.available | 2019-11-11T05:07:10Z | - |
dc.date.issued | 2000 | - |
dc.identifier.issn | 0265-0215 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/171627 | - |
dc.description.abstract | Background and goal of study: The prediction of difficult intubation to prevent unexpected results is a major concern of anaesthesiologists. A neural system could be used to develop a model to predict who is likely to be difficult in undergoing laryngeal intubation. The purpose of this study was to assess and to compare the usefulness of a neural network to predict difficult intubation by combining a patient's anatomical features. Materials and methods: After receiving approval from our ethical subcommittee, 462 ASA I-II patients (242 men, 220 women) aged 16 or older were prospectively studied. Mallampati's method which was modified by Samsoon and Young was carried out [1]. The airway was classified (oropharyngeal classification; OPC). In a supine position, hyo-mental distance (HD) with head in neutral, thyro-mental distance (TD), and interincisors distance (DI) on mouth opening with head fully extended, were all measured. We defined the cases of intubation trial more than 3 times in patients with laryngoscopic grade 3 or 4 as difficult intubation. Multilayer perceptron was used in the neural network. The number of nodes in a layer was selected automatically and the learning algorithm was the conjugate gradient method. Normalization of data was performed. Equalization of data was performed for the skewed class in a target variable. Results and discussion: Assignment to single signs had relatively high sensitivity and specificity. In various combinations, OPC and DI/HD resulted in increasing sensitivity. The neural network showed improved sensitivity compared to the ordinary single and combined performance of anatomical signs. | - |
dc.description.statementOfResponsibility | prohibition | - |
dc.language | English | - |
dc.publisher | Lippincott Williams & Wilkins | - |
dc.relation.isPartOf | European Journal of Anaesthesiology | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Prediction of difficult intubation using a neural network | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Anesthesiology and Pain Medicine (마취통증의학교실) | - |
dc.contributor.googleauthor | Kim, W. O. | - |
dc.contributor.googleauthor | Kil, H. K. | - |
dc.contributor.googleauthor | Kim, J. I. | - |
dc.contributor.localId | A00766 | - |
dc.relation.journalcode | J00807 | - |
dc.identifier.eissn | 1365-2346 | - |
dc.identifier.url | https://journals.lww.com/ejanaesthesiology/fulltext/2000/00002/prediction_of_difficult_intubation_using_a_neural.104.aspx | - |
dc.contributor.alternativeName | Kim, Won Oak | - |
dc.contributor.affiliatedAuthor | 김원옥 | - |
dc.citation.volume | 17 | - |
dc.citation.number | Suppl. 19 | - |
dc.citation.startPage | 32 | - |
dc.identifier.bibliographicCitation | European Journal of Anaesthesiology, Vol.17(Suppl. 19) : 32, 2000 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.