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研究生： 蘇庭揚
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
Predicting Medical Expenditure and Survivability of the Comorbidities Patients before Diagnosed with Lung Cancer by Bayesian Network
指導教授： 王孔政
Kung-Jeng Wang
口試委員: 王孔政
Kung-Jeng Wang
林希偉
Shi-Woei Lin
鄧乃嘉
Nai-Chia Teng
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： 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 Database, Kaplan–Meier estimate, conditional Gaussian Bayesian network
相關次數： 點閱：289  下載：2 
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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

    Abdous, B., & Berred, A. (2005). Mean residual life estimation. Journal of Statistical Planning and Inference, 132(1–2), 3-19. doi:https://doi.org/10.1016/j.jspi.2004.06.012
    Borchardt, R. A., & Torres, H. A. (2014). Challenges in managing hepatitis C virus infection in cancer patients. World Journal of Gastroenterology: WJG, 20(11), 2771.
    Bøttcher, S. G., & Dethlefsen, C. (2003). Deal: A Package for Learning Bayesian Networks. Journal of Statistical Software, 8(20), pp. 1-40. Retrieved from Deal: A package for learning bayesian networks.
    Cancer.Net. (2015, May 11). Screening for Hepatitis B Virus Before Starting Cancer Treatment. Retrieved from Cancer.Net: http://www.cancer.net/cancer-types/lung-cancer
    Chang, R.-E., & Chiang, T.-L. (1998). Risk Adjustment: a Key to Efficiency and Equity in the Health Insurance Market. Chinese Journal of Public Health, 17(5), pp. 373-380.
    Chang, R.-E., & Lai, C.-L. (2004). Risk Adjuster: the Basis for Capitation Payment. Taiwan Journal of Public Health, 23(2), pp. 91-99.
    Chang, R.-E., & Lai, C.-L. (2005). Use of Diagnosis-Based Risk Adjustment Models to Predict Individual Health Care Expenditure Under the National Health Insurance System in Taiwan. Journal of the Formosan Medical Association, 104(12), pp. 883-890.
    Chang, R.-E., Lin, W., Hsieh, C.-J., & Chiang, T.-L. (2002). Healthcare Utilization Patterns and Risk Adjustment Under Taiwan's National Health Insurance System. Journal of the Formosan Medical Associatio, 101(1), 52-59.
    Cheng, T.-M. (2015). Taiwan’s Health Care System: The Next 20 years. Taiwan-U.S. Quarterly Analysis(17). Retrieved from http://www.brookings.edu/research/opinions/2015/05/14-taiwan-national-healthcare-cheng
    Christy Brogan (2015) Kidney Failure Due to Cancer & Chemotherapy. Retrieved from http://www.livestrong.com/article/142238-kidney-failure-due-cancer-chemotherapy/
    Clinical treatment results (2017, May 11). Clinical treatment results. Retrieved from http://www.kfsyscc.org/research-and-edu/clinical-practice/lung-cancer/
    Cobb, B. R., Rumi, R., & Salmeron, A. (2007). Bayesian Network Models with Discrete and Continuous Variables. Advances in Probabilistic Graphical Models, Studies in Fuzziness and Soft Computing, 214, pp. 81-102.
    Corral, J., Espinàs, J. A., Cots, F., Pareja, L., Solà, J, Font, R., & Borràs, J. M. (2015). Estimation of Lung Cancer Diagnosis and Treatment Costs Based on a Patient-Level Analysis in Catalonia (Spain). BMC Health Services Research, 15(70), pp. 1-10.
    Cucciare, M. A., & O’Donohue, W. (2006). Predicting Future Healthcare Cost: How Well does Risk-Adjustment Work? Journal of Health Organization and Management, 20(2), pp. 150-162.
    Ferlay, J., Soerjomataram, I., Ervik, M., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D. M., Forman, D. & Bray, F. (2014). Cancer Incidence and Mortality Worldwide: Sources, Methods and Major Patterns in GLOBOCAM 2012. International Journal of Cancer, 136(5), pp. 359-386
    Fuch, V. R. (1968). The Growing Demand for Medical Care. The New England Journal of Medicine, 279, pp. 190-195.
    Health Promotion Administration, Ministry of Health and Welfare. (2015). 2015 Health Promotion Administration Annual Report. Taipei: Health Promotion Administration, Ministry of Health and Welfare.
    Health Promotion Administration, Ministry of Health and Welfare. (2016). 2015 Health Promotion Administration Annual Report. Taipei: Health Promotion Administration, Ministry of Health and Welfare.
    Huan, H.-L., Kung, P.-T., Chiu, C.-F., Wang, Y.-H., & Tsai, W.-C. (2014). Factors Assciated with Lung Cacner Patients Refusing Treatment and Their Survival: A National Cohort Study under a Universal Health Insurance in Taiwan. PLoS ONE, 9(7), pp. 1-10.
    Iachina, M., Green, A., & Jakobsen, E. (2014). The direct and indirect impact of comorbidity on the survival of patients with non-small cell lung cancer: a combination of survival, staging and resection models with missing measurements in covariates. BMJ Open, 4(2).
    Islam, K. M. M., Jiang, X., Anggondowati, T., Lin, G., & Ganti, A. K. (2015). Comorbidity and Survival in Lung Cancer Patients. Cancer Epidemiology Biomarkers & Prevention, 24(7), 1079.
    Janssen-Heijnen, M. L. G., Schipper, R. M., Razenberg, P. P. A., Crommelin, M. A., & Coebergh, J.-W. W. (1998). Prevalence of co-morbidity in lung cancer patients and its relationship with treatment: A population-based study. Lung Cancer, 21(2), 105-113. doi:https://doi.org/10.1016/S0169-5002(98)00039-7
    Julie Grisham (2013, Nov. 26) How does having diabetes affect my cancer treatment? Retrieved from https://www.mskcc.org/blog/how-does-having-diabetes-affect-my-treatment
    Ko, Y.-C., Lee, C.-H., Chen, M.-J., Huang, C.-C., Chang, W.-Y., Lin, H.-J., Wang, H.-Z., & Chang, P.-Y. (1997). Risk factors for primary lung cancer among non-smoking women in Taiwan. International Journal of Epidemiology, 26(1), pp. 24-31.
    Lauritzen, S. L. (1992). Propagation of Probabilities, Means, and Variances in Mixed Graphical Association Models. Journal of the American Statistical Association, 87(420), pp. 1098-1108.
    Lauritzen, S. L., & Jensen, F. (2001). Stable Local Computation with Conditional Gaussian Distributions. Statistics and Computing, 11, pp. 191-203.
    Lauritzen, S. L., & Wermuth, N. (1989). Graphical Models for Associations between Variables, some of which are Qualitative and some Quantitative. The Annals of Statistics, 17(1), pp. 31-57.
    Leduc, C., Antoni, D., Charloux, A., Falcoz, P.-E., & Quoix, E. (2017). Comorbidities in the management of patients with lung cancer. European Respiratory Journal, 49(3).
    Lin, W., Chang, R.-E, Hsieh, C.-J., Yaung, C.-L., & Chiang, T.-L. (2003). Development of a Risk-Adjusted Capitation Model Based on Principal Inpatient Diagnoses in Taiwan. Journal of the Formosan Medical Association, 102(9), pp. 637-643.
    Lindgren, A., Pukkala, E., Nissinen, A., & Tuomilehto, J. (2003). Blood pressure, smoking, and the incidence of lung cancer in hypertensive men in North Karelia, Finland. American journal of epidemiology, 158(5), 442-447.
    Lucena-Moya, P. J., van der Gaaq, L. C., & Abu-Hanna, A. (2004). Bayesian Networks in Biomedicine and Health-Care. Artificial Intelligence in Medicine, 30(3), pp. 201-214.
    Michael, A. C., & William, O. D. (2006). Predicting future healthcare costs: how well does risk‐adjustment work? Journal of Health Organization and Management, 20(2), 150-162. doi:10.1108/14777260610661547
    Ministry of Health and Welfare. (2014, 12). National Health Insurance Annual Report. Taipei: Ministry of Health and Welfare.
    Omachi, T. A., Gregorich, S. E., Eisner, M. D., Penaloza, R. A., Tolstykh, I. V., Yelin, E. H., Iribarren, C., Dudley, R. A., & Blanc,P. D. (2013). Risk Adjustment for Health Care Financing in Chronic Disease: What Are We Missing by Failing to Account for Disease Severity? Medical Care, 51(8), pp. 740-747.
    Pinter, M., Trauner, M., Peck-Radosavljevic, M., & Sieghart, W. (2016). Cancer and liver cirrhosis: implications on prognosis and management. ESMO open, 1(2), e000042.
    Pope, G. C., Ellis, R. P., Ash, A. S., Liu, C.-F., Ayanian, J. Z., & Bates, D. W. (2000). Principle Inpatient Diagnostic Cost Group Model for Medicare Risk Adjustment. Health Care Financing Review, 21(3), pp. 93-118.
    Ruckdeschel, J. C., Codish, S. D., Stranahan, A., & McKneally, M. F. (1972). Postoperative empyema improves survival in lung cancer: documentation and analysis of a natural experiment. New England Journal of Medicine, 287(20), 1013-1017.
    Samet, J. M., Tang, E. A., Boffetta, P., Hannan, L. M. , Marston, S. O., Thun, M. J., & Ruding, C. M. (2009). Lung cancer in never smokers: clinical epidemiology and environmental risk factors. Clinical Cancer Research, 15(18), pp. 5626-5645.
    Scutari, M. (2010). Learning Bayesian Networks with the bnlearn R Package. Journal of Statistical Software, 35(3), pp. 1-22.
    Scutari, M. (2015). Retrieved from bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference: http://www.bnlearn.com/
    Sekine, Y., Katsura, H., Koh, E., Hiroshima, K., & Fujisawa, T. (2012). Early detection of COPD is important for lung cancer surveillance. European Respiratory Journal, 39(5), 1230-1240.
    Silva, Denise Rossato, Valentini Junior, Dirceu Felipe, Muller, Alice Manica, Almeida, Carlos Podalirio Borges de, & Dalcin, Paulo de Tarso Roth. (2013). Pulmonary tuberculosis and lung cancer: simultaneous and sequential occurrence. Jornal Brasileiro de Pneumologia, 39(4), 484-489. https://dx.doi.org/10.1590/S1806-37132013000400013
    Tammemagi, C. M., Neslund-Dudas, C., Simoff, M., & Kvale, P. (2003). Impact of comorbidity on lung cancer survival. International Journal of Cancer, 103(6), 792-802. doi:10.1002/ijc.10882
    Wang, B.-Y., Huang, J.-Y., Cheng, C.-Y., Lin, C.-H., Ko, J.-L., & Liaw, Y.-P. (2013). Lung Cancer and Prognosis in Taiwan: A Population-Based Cancer Registry. Journal of Thoracic Oncology, 8(9), 1128-1135. doi:https://doi.org/10.1097/JTO.0b013e31829ceba4

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