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研究生： 陳俊霖
Jyun-Lin Chen
論文名稱： 以連續貝氏網為基礎之肺癌病人成本與存活預測模式
Cost and Survival Prognosis Model for Lung Cancer Patients: A Continuous Gaussian Bayesian Network Approach
指導教授： 王孔政
Kung-Jeng Wang
口試委員: 郭人介
Ren-Jieh Kuo
胡國琦
Gwo-Chi Hu
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： 141
中文關鍵詞： 台灣健保資料庫 、醫療保健支出 、連續高斯貝氏網路 、風險校正 、存活分析
外文關鍵詞： NHI databank, healthcare expenditure, continuous Gaussian Bayesian network, risk adjustment, survivability
相關次數： 點閱：234  下載：3 
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    • 癌症自1982年以來皆是台灣主要死因，據統計2013年有44,791位病患死於癌症，佔總死亡人數的29%。其中，肺癌更為癌症死因之首，因肺癌過世的病患佔了癌症患者的19.77%，在初期的治療成本更高居各種常見癌症之冠，種種跡象顯示，肺癌病患的醫療資源需求應被更深入的探討。風險校正在處理病患需求及政府資源規劃上能取得有效的平衡，向來被視為評估個人醫療需求的有利工具。本研究使用台灣之健保資料庫，並應用高斯貝氏網路評估肺癌病患的生存時間與未來醫療費用預測，本研究以文獻探討為基礎，整理歸納相關風險因子及針對肺癌病患發展適當的評估因子，並特別考慮了癌症嚴重程度分期對於模型的影響，此應用在過去研究中仍屬罕見。本研究所提出之高斯貝氏網路於預測肺癌病患生存時間有良好成果，從第一期至第四期之調整R2分別達到93.574%、86.827%、67.222%、52.940%之表現；在預測未來醫療費用方面，從第一期至第四期之調整R2分別達到32.63%、50.301%、50.363%、66.578%之表現，此結果相較過去文獻已有明顯成長。本研究亦針對生存時間與未來醫療費用提出機率密度函數模型，決策制定者可應用本研究成果評估肺癌病患醫療資源需求，肺癌病患將可獲得更妥適的醫療資源分派。

    In Taiwan, cancer has always become one of the leading cause of death since 1982. Ministry of Health and Welfare mortality statistics showed that 44,791 people died of cancer in 2013, accounting for 29 percent of all deaths. Furthermore, lung cancer is the leading cause of mortalities no matter in men or women in 2013, which accounted 19.77% of all cancer deaths. The resources for lung cancer patients’ medical care should be considered much deep. Risk adjustment deals with the issues of equity and efficiency separately by establishing a risk equalization, which is seen as an effective way to evaluate individual medical requirement. This study presented a continuous Gaussian Bayesian network model to evaluate lung cancer patients’ survival time and expenditure from Taiwan’s National Health Insurance databank. Based on previous literatures, we summarized related risk adjustment outcomes, and also provided an overview of factors selection of lung cancer. In addition, this study presented the severity stages of risk adjustment model. For survival time estimation, the adjusted R2 performed 93.574% of stage I, 86.827% of stage II, 67.222% of stage III, and 52.940% of stage IV. For expenditure estimation, the adjusted R2 performed 32.63% of stage I, 50.301% of stage II, 50.363% of stage III, and 66.578% of stage IV. Compared with previous literatures, this study successfully increased the predictive power of risk adjustment model by using a continuous Gaussian Bayesian network. This study also performed the probability density function for all factors, as well as healthcare expenditure and overall survivability prediction. Public decision maker can utilize the proposed model to measure the lung cancer patients. According to this study, requirement planning of lung cancer patients can be evaluate properly.

    摘要 I ABSTRACT II 誌謝 III TABLE OF CONTENTS IV LIST OF TABLES VI LIST OF FIGURES VII CHAPTER 1. Introduction 1 1.1 Research Background 1 1.2 Research Motivation 5 1.3 Research Objective 6 1.4 Research Limitation 7 1.5 Thesis Structure 7 CHAPTER 2. Literature Review 9 2.1 Risk Adjustment Factor 9 2.2 Lung Cancer Risk Factor 14 2.3 Bayesian Network 16 CHAPTER 3. Materials and Model Development 17 3.1 Data Source and Study Population 17 3.2 Variables 20 3.3 Modeling 29 3.3.1 Bayesian Network Construction 29 3.3.2 Causal Relationship 32 CHAPTER 4. Experiments and Results 37 4.1 Residual Lifetime Evaluation 38 4.2 Bayesian Network Estimation 40 4.2.1 Survival Time Estimation 41 4.2.2 Medical Cost Estimation 44 4.3 Inference and findings 47 4.3.1 Inference for Factors except Future Expenditure and Survival Time 47 4.3.2 Inference for Future Expenditure and Survival Time 52 CHAPTER 5. Conclusion and Future Research 58 5.1 Discussion and Conclusion 58 5.2 Future Research 61 References 62 Appendix A: Variables definition in table 3-3 66 Appendix B: Bayesian network structure by greedy search 81 Appendix C: Resulting conditional relationship from stage I(cost) 85 Appendix D: Resulting conditional relationship from stage I (survival) 91 Appendix E: Resulting conditional relationship from stage II (cost) 97 Appendix F: Resulting conditional relationship from stage II (survival) 104 Appendix G: Resulting conditional relationship from stage III (both cost & survival) 111 Appendix H: Resulting conditional relationship from stage IV (cost) 114 Appendix I: Resulting conditional relationship from stage IV(survival) 122

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