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研究生:蘇庭揚
研究生(外文):Ting-Yang Su
論文名稱:以貝式網模型預測共病肺癌患者之醫療費用與存活時間
論文名稱(外文):Predicting Medical Expenditure and Survivability of the Comorbidities Patients before Diagnosed with Lung Cancer by Bayesian Network
指導教授:王孔政王孔政引用關係
指導教授(外文):Kung-Jeng Wang
口試委員:王孔政林希偉鄧乃嘉
口試委員(外文):Kung-Jeng WangShi-Woei LinNai-Chia Teng
口試日期:2017-06-19
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:工業管理系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:69
中文關鍵詞:全民健康保險研究資料庫條件高斯貝式網存活分析
外文關鍵詞:National Health Insurance Research DatabaseKaplan–Meier estimateconditional Gaussian Bayesian network
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肺癌已被世界認為是腫瘤致死的主要因子。在台灣,肺癌也最常見的癌症死因之一,其醫療成本更是2015年十大癌症中最高的。本研究資料取自於全民健康保險研究資料庫,針對1996~2010年有被診斷肺癌前的共病症肺癌患者,並從中挑選有2,875人作為實驗數據。此外,本研究利用條件高斯貝式網路來評估患者的存活時間與醫療成本,且使用風險因子來建構貝式網模型,以及存活分析KM法調整存活時間。結果顯示,存活時間的預測結果R2為62.37%,而醫療成本的預測結果R2為23.84%。本模型不僅能用於預測肺癌患者的存活時間和醫療費用,還能計算出其他醫療相關的後驗機率。
Lung cancer is already considered to be the leading cause of tumor-related death in the world. It is also the most common cancer death in Taiwan and medical cost of lung cancer is the highest among top ten cancer in 2015. This study collected cases of which patients were diagnosed with comorbidities before diagnosed lung cancer from 1996 to 2010 in Taiwan National Health Insurance Research Database, and 2,875 cases were selected as experimental data. In addition, conditional Gaussian Bayesian network was proposed to evaluate survival time and medical cost of experimental data. Risk factors were used to construct Bayesian network model and Kaplan–Meier estimate was used to adjust censored data. The R2 of result of survival time prediction is 62.37% and the R2 of result of medical expenditure prediction is 23.84%. The proposed model is not only useful to predict the survival probability and medical expenditure of patients with lung cancer, but it can also calculate the posterior probabilities of variety of medical-related query.
Abstract I
摘要 II
誌謝 III
Table of Contents IV
List of Tables V
List of Figures VI
Chapter 1. Introduction 1
Chapter 2 Methods 4
2.1 Conditional Gaussian Bayesian network 4
2.2 Kaplan–Meier estimate 5
Chapter3 Materials and Model Development 6
3.1 Risk factor 6
3.2 Study population and data processing 7
3.3 Comorbidity risk factor selection 11
3.4 Treatment definition 13
3.5 Definition of survival time and medical expenditure 14
3.6 Model Development 15
Chapter4. Experiment and Results 18
4.1 Residual lifetime estimation 19
4.2 Bayesian network representation 22
4.2.1 Probability estimation 22
4.2.2 Prediction accuracy of survival time in model 1 23
4.2.3 Prediction accuracy of survival time in model 3 25
4.2.4 Prediction accuracy of medical cost in model 1 27
4.2.3 Prediction accuracy of survival time in model 3 29
4.3 Query modeling and illustration using CGBN 32
Chapter5. Conclusion and Future Research 34
5.1 Conclusion 34
5.2 Future research 36
References 37
Appendix A 41
Appendix B 43
Appendix C 57
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