Predicting postoperative vomiting among orthopedic patients receiving patient-controlled epidural analgesia using SVM and LR

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題名:	Predicting postoperative vomiting among orthopedic patients receiving patient-controlled epidural analgesia using SVM and LR
作者:	龔存雄;Wu, Hsin-Yun;Gong, Cihun-Siyong Alex;Lin, Shih-Pin;Chang, Kuang-Yi;Tsou, Mei-Yung;Ting, Chien-Kun
貢獻者:	資訊電機學院電機工程學系
關鍵詞:	692/308/575;692/700/1750;Aged;Aged, 80 and over;Algorithms;Analgesia, Epidural - methods;Analgesia, Patient-Controlled - methods;Analgesics;Anesthetics, Local;Area Under Curve;Bone surgery;Bupivacaine;Catheters;Drug dosages;Epidural;Female;Fentanyl;Humanities and Social Sciences;Humans;Logistic Models;Male;Middle Aged;multidisciplinary;Nausea;Orthopedic Procedures;Orthopedics;Pain Measurement;Pain, Postoperative - physiopathology;Pain, Postoperative - prevention & control;Patient satisfaction;Postoperative Nausea and Vomiting - diagnosis;Postoperative Nausea and Vomiting - prevention & control;Prediction models;Prognosis;Random variables;Regression analysis;Retrospective Studies;ROC Curve;Science;Support Vector Machine;Support vector machines;Vomiting
日期:	2016-06-01
上傳時間:	2026-04-23 14:32:26 (UTC+8)
出版者:	Nature Publishing Group;London: Springer Science and Business Media LLC
摘要:	摘要： AbstractPatient-controlled epidural analgesia (PCEA) has been applied to reduce postoperative pain in orthopedic surgical patients. Unfortunately, PCEA is occasionally accompanied by nausea and vomiting. The logistic regression (LR) model is widely used to predict vomiting, and recently support vector machines (SVM), a supervised machine learning method, has been used for classification and prediction. Unlike our previous work which compared Artificial Neural Networks (ANNs) with LR, this study uses a SVM-based predictive model to identify patients with high risk of vomiting during PCEA and comparing results with those derived from the LR-based model. From January to March 2007, data from 195 patients undergoing PCEA following orthopedic surgery were applied to develop two predictive models. 75% of the data were randomly selected for training, while the remainder was used for testing to validate predictive performance. The area under curve (AUC) was measured using the Receiver Operating Characteristic curve (ROC). The area under ROC curves of LR and SVM models were 0.734 and 0.929, respectively. A computer-based predictive model can be used to identify those who are at high risk for vomiting after PCEA, allowing for patient-specific therapeutic intervention or the use of alternative analgesic methods. 其他題名： Sci Rep 出版者： London: Springer Science and Business Media LLC 出版日期： 2016-06-01 出處： Scientific Reports, 2016-06, Vol.6 (1), p.27041-, Article 27041 資源來源： Publicly Available Content Database (Proquest) 版權： The Author(s) 2016 版權： Copyright Nature Publishing Group Jun 2016 版權： Copyright © 2016, Macmillan Publishers Limited 2016 Macmillan Publishers Limited 識別號： ISSN: 2045-2322 識別號： EISSN: 2045-2322 識別號： DOI: 10.1038/srep27041 識別號： PMID: 27247165
顯示於類別:	[電機工程學系] 期刊論文

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