Predicting postoperative vomiting for orthopedic patients receiving patient-controlled epidural analgesia with the application of an artificial neural network

NCUIR > College of Electrical Engineering & Computer Science > Department of Electrical Engineering > journal & Dissertation > Item 987654321/108032

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Title:	Predicting postoperative vomiting for orthopedic patients receiving patient-controlled epidural analgesia with the application of an artificial neural network
Authors:	龔存雄;Yu, Lu;Gong, Cihun-Siyong Alex;Ting, Chien-Kun;Tsou, Mei-Yung;Chang, Kuang-Yi;Shen, Chih-Long;Lin, Shih-Pin
Contributors:	資訊電機學院電機工程學系
Keywords:	Analgesia;Analgesia, Epidural - adverse effects;Analgesia, Patient-Controlled - adverse effects;Analgesics;Anesthesiology;Bone surgery;Catheters;Computer engineering;Confidence intervals;Female;Humans;Logistic Models;Male;Models, Statistical;Nerve Net;Neural networks;Neural Networks (Computer);Orthopedic Procedures - adverse effects;Patient satisfaction;Postoperative Complications - chemically induced;Postoperative Complications - pathology;Postoperative Nausea and Vomiting - chemically induced;Postoperative Nausea and Vomiting - pathology;Software;Studies;Variables;Vomiting
Date:	2014-01-01
Issue Date:	2026-04-23 14:32:29 (UTC+8)
Publisher:	Hindawi Publishing Corporation;Cairo, Egypt: Hindawi Puplishing Corporation
Abstract:	摘要： Patient-controlled epidural analgesia (PCEA) was used in many patients receiving orthopedic surgery to reduce postoperative pain but is accompanied with certain incidence of vomiting. Predictions of the vomiting event, however, were addressed by only a few authors using logistic regression (LR) models. Artificial neural networks (ANN) are pattern-recognition tools that can be used to detect complex patterns within data sets. The purpose of this study was to develop the ANN based predictive model to identify patients with high risk of vomiting during PCEA used. From January to March 2007, the PCEA records of 195 patients receiving PCEA after orthopedic surgery were used to develop the two predicting models. The ANN model had a largest area under curve (AUC) in receiver operating characteristic (ROC) curve. The areas under ROC curves of ANN and LR models were 0.900 and 0.761, respectively. The computer-based predictive model should be useful in increasing vigilance in those patients most at risk for vomiting while PCEA is used, allowing for patient-specific therapeutic intervention, or even in suggesting the use of alternative methods of analgesia. 其他題名： Biomed Res Int 出版者： Cairo, Egypt: Hindawi Puplishing Corporation 出版日期： 2014-01-01 出處： BioMed research international, 2014-01, Vol.2014 (2014), p.1-6 資源來源： Publicly Available Content Database 版權： Copyright © 2014 Cihun-Siyong Alex Gong et al. 版權： COPYRIGHT 2014 John Wiley & Sons, Inc. 版權： Copyright © 2014 Cihun-Siyong Alex Gong et al. Cihun-Siyong Alex Gong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 版權： Copyright © 2014 Cihun-Siyong Alex Gong et al. 2014 識別號： ISSN: 2314-6133 識別號： ISSN: 2314-6141 識別號： EISSN: 2314-6141 識別號： DOI: 10.1155/2014/786418 識別號： PMID: 25162027
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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