教師資料查詢 | 類別: 期刊論文 | 教師: 葛煥昭 Keh Huan-chao (瀏覽個人網頁)

標題:Intelligent Postoperative Morbidity Prediction of Heart Disease Using Artificial Intelligence Techniques
學年99
學期1
出版(發表)日期2010/12/24
作品名稱Intelligent Postoperative Morbidity Prediction of Heart Disease Using Artificial Intelligence Techniques
作品名稱(其他語言)
著者Hsieh, Nan-Chen; Hung, Lun-Ping; Shih, Chun-Che; Keh, Huan-Chao; Chan, Chien-Hui
單位淡江大學資訊工程學系
出版者New York: Springer New York LLC
著錄名稱、卷期、頁數Journal of Medical Systems 36(3), pp.1809-1820
摘要Endovascular aneurysm repair (EVAR) is an advanced minimally invasive surgical technology that is helpful for reducing patients’ recovery time, postoperative morbidity and mortality. This study proposes an ensemble model to predict postoperative morbidity after EVAR. The ensemble model was developed using a training set of consecutive patients who underwent EVAR between 2000 and 2009. All data required for prediction modeling, including patient demographics, preoperative, co-morbidities, and complication as outcome variables, was collected prospectively and entered into a clinical database. A discretization approach was used to categorize numerical values into informative feature space. Then, the Bayesian network (BN), artificial neural network (ANN), and support vector machine (SVM) were adopted as base models, and stacking combined multiple models. The research outcomes consisted of an ensemble model to predict postoperative morbidity after EVAR, the occurrence of postoperative complications prospectively recorded, and the causal effect knowledge by BNs with Markov blanket concept.
關鍵字Endovascular aneurysm repair (EVAR);Postoperative morbidity;Ensemble model ;Machine learning;Markov blanket
語言英文(美國)
ISSN0148-5598
期刊性質國外
收錄於SCI;
產學合作
通訊作者Hsieh, Nan-Chen
審稿制度
國別美國
公開徵稿
出版型式,紙本
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