期刊論文
| 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 | en_US |
| ISSN | 0148-5598 |
| 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 | 否 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/98387 ) |