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研究生： 陳昱瑋
Yu-Wei Chen
論文名稱： 應用貝氏網路於肺癌併骨骼移轉之預後推論
Applying Bayesian Networks to Prognostic Inferences for Bone Metastasis from Lung Cancer
指導教授： 林希偉
Shi-Woei Lin
口試委員: 王孔政
Kung-Jeng Wang
謝志宏
Chih-Hung Hsieh
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2015
畢業學年度： 103
語文別： 英文
論文頁數： 94
中文關鍵詞： 貝氏網路 、肺癌 、移轉 、簡單貝氏模型 、樹狀貝氏模型 、CaMML
外文關鍵詞： Bayesian network, bone metastasis, lung cancer, Tree augmented Naive Bayes, Naive Bayes model, CaMML
相關次數： 點閱：342  下載：4 
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    • 為了為老化社會中的癌症醫療提供更佳的預後分析及決策支援，在本研究中，我們應用貝氏網路，針對臺灣常見肺癌之移轉（骨轉移）來做描述與預測，並且進行建模及推論。研究中用來建立貝氏網路模型的資料庫包含從1996年到2010年間所收集的臺灣肺癌患者資料，共44,883例。研究中並透過交叉驗證比較包含簡單貝氏模型、樹狀貝氏模型、專家判斷模型及CaMML（Causal discovery via Maximum Message Length）模型。實驗結果指出能夠結合自動學習與專家判斷的CaMML結構學習法得來的模型的預後能力超越多數其他模型，同時又保有易於詮釋的優點。本研究結果不僅可以幫助醫療決策者預測及評估移轉的風險，並且可對於肺癌移轉（骨）做診斷及推論，進一步改善醫療決策的品質。

    In order to improve the prognostic prediction and decision support for cancer medicine, Bayesian networks were applied to model the mechanism of the occurrence of bone metastasis from lung cancer and to perform uncertainty inferences in this thesis study. A nationwide database containing 44,883 cases of cancer patients who primarily diagnosed with lung cancer from 1996-2010 in Taiwan was used to build the network. Several experiments were carried out with different Bayesian network topologies including Naive Bayes model, Tree Augmented Naive Bayes model (TAN), Expert Judgment model and the model obtained by using Causal discovery via Maximum Message Length (CaMML) to predict the occurrence of metastasis. Results show that the hybrid model (of CaMML with temporal tier information) which integrates expert judgment and automatic learning into the Bayesian network structure learning outperforms many models in terms of predictive accuracy. This model can help physicians to predict the probability of any query given limit evidences and get the uncertainty inference of bone metastasis of lung cancer. Results from the study may also provide useful recommendations and guidelines for how to utilize our health care resources for lung cancer medicine

    摘要 Abstract 誌謝 Contents List of Tables List of Figures Chapter 1 Introduction 1.1 Research Background 1.2 Research Motivation 1.3 Research Purposes and Framework Chapter 2 Literature Review 2.1 Bayesian Networks 2.1.1 Structure and Parameter Learning 2.2 Bayesian Network Applications in Medicine 2.2.1 Bayesian Network Application in Ontology or Cancer Study 2.2.2 Bone Metastasis of Lung Cancer and Bayesian Networks Applications Specifically in Lung Cancer Study Chapter 3 Materials and Methods 3.1 Variables and Data 3.1.1 Pre-processing the NHI dataset 3.1.2 Re-sampling Techniques 3.2 Bayesian Networks Model 3.2.1 Definition 3.2.2 Structure Learning 3.2.3 Parameter Learning 3.3 Benchmark Methods 3.4 Performance Evaluation of Classifiers Chapter 4 Experiments and Results 4.1 Explanatory Graphical Model 4.1.1 Naive Bayes 4.1.2 Tree Augmented Naive Bayes (TAN) 4.1.3 Expert Judgment 4.1.4 CaMML without Expert prior 4.1.5 CaMML with Tier 4.2 Assessment on Predictive Performance 4.3 Sensitivity Analysis 4.3.1 Sensitivity of Model Performance to Different Over-sampling Technique 4.3.2 Different processing steps for dataset 4.3.3 Comprehensive Sensitivity Analysis (by Changing More Than Two Settings Simultaneously) 4.4 Discussion Chapter 5 Inference 5.1 Predictive Reasoning 5.2 Most Probable Explanation Chapter 6 Conclusions and Future Research Reference Appendix

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