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系統識別號 U0002-0706201011592700
中文論文名稱 以人工智慧識別腹主動脈瘤手術危險因子及死亡率預測
英文論文名稱 Risk factor identification and mortality prediction in abdominal aortic surgery using artificial intelligence
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 98
學期 2
出版年 99
研究生中文姓名 詹千慧
研究生英文姓名 Chien-Hui Chan
學號 697410131
學位類別 碩士
語文別 英文
口試日期 2010-05-28
論文頁數 54頁
口試委員 指導教授-葛煥昭
委員-蔣定安
委員-葛煥昭
委員-謝楠楨
中文關鍵字 主動脈瘤修復  手術後併發症  集成式模型  機器學習  馬可夫覆蓋 
英文關鍵字 Aortic aneurysm repair  postoperative morbidity  ensemble model  machine learning  Markov blanket 
學科別分類 學科別應用科學資訊工程
中文摘要 本研究提出一集成式腹主動脈瘤手術後併發症預測模型,本模型以1994年至2008年間進行腹主動脈瘤手術之病患資料進行訓練,本研究結果包括一集成式術後併發症預測模型、術後併發症預測記錄及因果關係決策規則,本模型所計算出之併發症機率與實際發生併發症事實比較,並以接收操作特徵曲線(ROC curve) 進行術後併發症預測模型之準確性評估。經過一系列測試,貝式網路(BN)、類神經網路(NN)及支持向量機(SVM)所集成之模型對於腹主動脈瘤修復術術後併發症預測可提供良好的效能。此外,貝式網路之馬可夫覆蓋提供了以粒子計算所產生的基本決策規則而自然形成之因果關係特徵選取。
英文摘要 This study proposes an ensemble model to predict postoperative morbidity after abdominal aortic surgery. The ensemble model was developed using a training set of consecutive patients who underwent abdominal aortic aneurysm (AAA) repair between 1994 and 2008. The research outcomes consisted of an ensemble model to predict postoperative morbidity, the occurrence of postoperative complications prospectively recorded, and the causal-effect decision rules. The probabilities of complication calculated by the model were compared to the actual occurrence of complications and a receiver operating characteristic (ROC) curve was used to evaluate the accuracy of postoperative morbidity prediction. In this series, the ensemble of BN, NN and SVM models offered satisfactory performance in predicting postoperative morbidity after AAA repair. Moreover, the Markov blankets of BN allow a natural form of causal-effect feature selection, which provides a basis for screening decision rules generated by granular computing.
論文目次 Table of Contents
Table of Contents III
List of Figures IV
List of Tables V
Chapter 1 Introduction 1
1.1 Research Objectives 4
1.2 Organization of the Dissertation 6
Chapter 2 Review of the Related Work 7
2.1 Data Mining in Medicine 7
2.2 Abdominal Aortic Aneurysms 11
Chapter 3 Methods and Procedures 15
3.1 Materials and Data Preprocessing 16
3.2 Discretization Techniques 17
3.3 Ensemble model 23
3.4 Bayesian network 24
3.5 Rough Set 27
Chapter 4 Results and Discussion 30
4.1 Experiments Results and Analysis 30
4.2 Evaluation the Model 36
Chapter 5 Conclusions and Future Research 39
Reference 41
Appendix A 45

List of Figures
Fig. 1. The proposed architecture 5
Fig. 2. Steps of data mining process 8
Fig. 3. Normal aorta and aortic aneurysms 11
Fig. 4. The trapezoidal fuzzy set of AAA_Size and Creatine 22
Fig. 5. A Markov blanket for EVAR 31
Fig. 6. The partial causality of MB with probability distributions 32
Fig. 7. Fuzzy quantifiers for linguistic summaries 35
Fig. 8. The results of the postoperative morbidity prediction 38

List of Tables
Table 1. Variables used to predict the postoperative morbidity 18
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