肺癌患者存活時間與醫療費用之動態貝式網路預測模型

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研究生：	Muniroh Muniroh
論文名稱：	肺癌患者存活時間與醫療費用之動態貝式網路預測模型 Predicting Survivability and Medical Expenditure of Lung Cancer Patients Using Dynamic Bayesian Network
指導教授：	王孔政 Kung-Jeng Wang
口試委員:	林希偉 Shi-Woei Lin 郭人介 Ren-Jieh Kuo
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：	2019
畢業學年度：	107
語文別：	英文
論文頁數：	75
中文關鍵詞：	动态贝叶斯网络、肺癌、生存能力、医疗支出
外文關鍵詞：	Dynamic Bayesian network, lung cancer, survivability, medical expenditure
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上一筆

估计在2018年死亡人数估计为960万，癌症是自1982年以来死亡的主要原因。癌症患者人数的增加导致医疗费用从660亿新台币到维持医疗质量和成本平衡，风险调整必须应用以实现改进。该研究旨在构建动态贝叶斯网络（DBN）模型，以预测肺癌的生存能力和医疗支出。 Kaplan-Meier估计是为了获得患者的剩余寿命，我们使用风险因素来构建我们的生存性预测模型，并且由于数据是纵向的，因此提出的DBN将数据视为时间集。 DBN模型具有预测值，R2值为17.43％; 35.79％;分别为41.35％DBN模型患者的生存期为4年，而3个基准模型的患者生存期为5年。对于医疗支出，我们的DBN模型与过去的研究相比具有70.87％的高R2值。患者的平均支付医疗费用为41,517新台币，而其他三个基准型号为41,678新台币。所提出的模型不仅可以准确地预测生存性和医疗费用，而且可以计算各种医学相关查询的后验概率。

Accounting for an estimated 9.6 million deaths in 2018, cancer is a leading cause of death worldwide. In Taiwan, cancer also became the leading cause of death since 1982. The increasing of cancer patients, lead to medical care cost from NT$ 66 billion to NT$ 81.5 billion and most of the medical care cost for cancer is lung cancer and liver cancer since thus two were common type of cancer in Taiwan. To maintain the quality of healthcare and cost balance, risk adjustment must be applied to achieve the improvement. This study aims to construct a dynamic Bayesian network (DBN) model to predict lung cancer survivability and medical expenditure. Kaplan-Meier estimation was conduct to get residual life of patients, and we used risk factors to construct our prediction model for survivability and medical expenditure. Since the data is longitudinal, th proposed DBN treats the data as temporal set. The DBN model have the predicting value with the R2 value of 17.43%; 35.79%; 41.35% respectively in three stages. The resulting patients survival by the DBN model is 4 years while three benchmark models reorted 5 years. For the medical expenditure, our DBN model has the highet R2 value with 70.87%as compared to the past studies. The average of a patient to pay the medical expenditure is 41,517 NTD while the other three benchmark models are of 41,678 NTD. The proposed models can not only predict the survivability and medical expenditure with good accuracy, but calculate the posterior probability of a variety of medical related queries.

抽象        I
Abstract        II
Acknowledgment    III
Content of Table    VI
Content of Figure    VII
Chapter 1 Introduction    1
1.1 Research background    1
1.2 Research motivation and objective    3
1.3 Thesis structure    4
Chapter 2 Literature Review    5
2.1 Risk adjustment factors    5
2.2 Risk factors for lung cancer    6
2.3 Dynamic Bayesian network    9
2.4 Kaplan-Meier estimation    10
2.5    Summary of the chapter    11
Chapter 3 Materials and Model Development    12
3.1 Data source    12
3.2 Variables and modelling    15
3.3 Graphical Models    18
3.4 Evaluation criteria    23
Chapter 4 Experiments and Results    24
4.1 Data Analysis    24
4.2 Residual Life Estimation    26
4.3 Survivability Estimation    29
4.4 Medical Expenditure Estimation    31
4.5 Conditional Probability Table for DBN    32
Chapter 5 Conclusion and Future Research    41
5.1 Discussion and Conclusion    41
5.2 Future Research    42
References    44
Appendix A: Resulting Conditional Probability Table of Site in Baseline Study    48
Appendix B: Resulting Conditional Probability Table of Site in Follow-Up Study    49
Appendix C: Resulting Conditional Probability Table of Site in Last Study    53
Appendix D: The Conditional Probability Table for Each Node    57
The Conditional Probability Tables acoross three  stages?    58
Appendix E: Actual & Prediction    59

                                

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