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Survivability Prognosis for Lung Cancer Patients at Different Severity Stages by a Risk Factor-Based Bayesian Network Modeling

  • Patient Facing Systems
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Abstract

Lung cancer is a major reason of mortalities. Estimating the survivability for this disease has become a key issue to families, hospitals, and countries. A conditional Gaussian Bayesian network model was presented in this study. This model considered 15 risk factors to predict the survivability of a lung cancer patient at 4 severity stages. We surveyed 1075 patients. The presented model is constructed by using the demographic, diagnosed-based, and prior-utilization variables. The proposed model for the survivability prognosis at different four stages performed R2 of 93.57%, 86.83%, 67.22%, and 52.94%, respectively. The model predicted the lung cancer survivability with high accuracy compared with the reported models. Our model also shows that it reached the ceiling of an ideal Bayesian network.

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References

  1. Health Promotion Administration, Ministry of Health and Welfare. (2014). 2014 health promotion administration annual report. Taipei: Health Promotion Administration, Ministry of Health and Welfare.

  2. Lang, H.-C., and Wu, S.-L., Lifetime costs of the top five cancers in Taiwan. The European Journal of Health Economics 13(3):347–353, 2012.

    Article  PubMed  Google Scholar 

  3. Ministry of Health and Welfare. (2014, 12). National Health Insurance Annual Report. Taipei: Ministry of Health and Welfare.

  4. Chang, R.-E., and Chiang, T.-L., Risk adjustment: A key to efficiency and equity in the health insurance market. Chinese Journal of Public Health 17(5):373–380, 1998.

    Google Scholar 

  5. Chang, R.-E., Lin, W., Hsieh, C.-J., and Chiang, T.-L., Healthcare utilization patterns and risk adjustment under Taiwan's National Health Insurance System. Journal of the Formosan Medical Associatio 101(1):52–59, 2002.

    Google Scholar 

  6. Wang, K. J., Chen, K. H., Huang, S. H., and Teng, N. C., A prognosis tool based on fuzzy anthropometric and questionnaire data for obstructive sleep apnea severity. Journal of Medical Systems 40(4):110, 2016.

    Article  PubMed  Google Scholar 

  7. Chang, R.-E., and Lai, C.-L., Risk adjuster: The basis for capitation payment. Taiwan Journal of Public Health 23(2):91–99, 2004.

    Google Scholar 

  8. Rice, N., and Smith, P. C., Capitation and risk adjustment in health care financing: An international Progress report. The Milbank Quarterly 79(1):81–113, 2001.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. van de Ven, W. P. M. M., and Ellis, R. P., Risk adjustment in competitive health plan markets. In: Culyer, A. J. (Ed.), Handbool of health economics. Amsterdam: Elsevier Science, 2000, 755–845.

    Google Scholar 

  10. Howlader N, Noone AM, Krapcho M, Garshell J, Miller D, Altekruse SF, Kosary CL, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA (eds). (2014). SEER Cancer Statistics Review, 1975–2011. Retrieved from National Cancer Institute. Bethesda, MD, http://seer.cancer.gov/csr/1975_2011/, based on November 2013 SEER data submission, posted to the SEER web site.

  11. Ko, Y.-C., Lee, C.-H., Chen, M.-J., Huang, C.-C., Chang, W.-Y., Lin, H.-J., Wang, H.-Z., and Chang, P.-Y., Risk factors for primary lung cancer among non-smoking women in Taiwan. International Journal of Epidemiology 26(1):24–31, 1997.

    Article  CAS  PubMed  Google Scholar 

  12. Samet, J. M., Tang, E. A., Boffetta, P., Hannan, L. M., Marston, S. O., Thun, M. J., and Ruding, C. M., Lung cancer in never smokers: Clinical epidemiology and environmental risk factors. Clinical Cancer Research 15(18):5626–5645, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wang, B.-Y., Huang, J.-Y., Cheng, C.-Y., Lin, C.-H., Ko, J.-L., and Liaw, Y.-P., Lung Cancer and prognosis in Taiwan: A population-based Cancer registry. Journal of Thoracic Oncology 8(9):1128–1135, 2013.

    Article  PubMed  Google Scholar 

  14. Jensen, F. V., Introduction to Bayeisan network. Berlin: Springer, 1996.

    Google Scholar 

  15. Uusitalo, L., Advantages and challenges of Bayesian networks in environmental Modelling. Ecological Modeling 203:312–318, 2007.

    Article  Google Scholar 

  16. Lucas, P. J., van der Gaaq, L. C., and Abu-Hanna, A., Bayesian networks in biomedicine and health-care. Artificial Intelligence in Medicine 30(3):201–214, 2004.

    Article  PubMed  Google Scholar 

  17. Kaviarasi, R., Accuracy enhanced lung Cancer prognosis for improving patient survivability using proposed Gaussian classifier system. Journal of Medical Systems 43(7):201, 2019.

    Article  Google Scholar 

  18. Petousis, P., Han, S. X., Aberle, D., and Bui, A. A., Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network. Artificial Intelligence in Medicine 72:42–55, 2016.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J. F., and Hua, L., Data mining in healthcare and biomedicine: A survey of the literature. Journal of Medical Systems 36(4):2431–2448, 2012.

    Article  PubMed  Google Scholar 

  20. Cobb, B. R., Rumi, R., and Salmeron, A., Bayesian network models with discrete and continuous variables. Advances in Probabilistic Graphical Models, Studies in Fuzziness and Soft Computing 214:81–102, 2007.

    Article  Google Scholar 

  21. Lauritzen, S. L., Propagation of probabilities, means, and variances in mixed graphical association models. Journal of the American Statistical Association 87(420):1098–1108, 1992.

    Article  Google Scholar 

  22. Lucena-Moya, P., Brawata, R., Kath, J., Harrison, E., ElSawah, S., and Dyer, F., Discretization of continuous predictor variables in Bayesian networks: An ecological threshold approach. Environmental Modeling & Software 66:36–45, 2015.

    Article  Google Scholar 

  23. Lauritzen, S. L., and Wermuth, N., Graphical models for associations between variables, some of which are qualitative and some quantitative. The Annals of Statistics 17(1):31–57, 1989.

    Article  Google Scholar 

  24. Lauritzen, S. L., and Jensen, F., Stable local computation with conditional Gaussian distributions. Statistics and Computing 11:191–203, 2001.

    Article  Google Scholar 

  25. Cancer.Net. (2015). Cancer.Net. Retrieved from Cancer.Net: http://www.cancer.net/cancer-types/lung-cancer

  26. Chen, K.-H., Wang, K. J., Adrian, A. M., Wang, K. M., and Teng, N.-C., Diagnosis of brain metastases from lung cancer using a modified electromagnetism like mechanism algorithm. Journal of Medical Systems 40(1):35, 2016.

    Article  PubMed  Google Scholar 

  27. Wang, K. J., Chen, J. L., and Wang, K. M., Medical expenditure estimation by Bayesian network for lung cancer patients at different severity stages. Computers in Biology and Medicine 106:97–105, 2019.

    Article  PubMed  Google Scholar 

  28. 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

  29. Fuch, V. R., The growing demand for medical care. The New England Journal of Medicine 279:190–195, 1968.

    Article  Google Scholar 

  30. Chang, R.-E., and Lai, C.-L., 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):883–890, 2005.

    PubMed  Google Scholar 

  31. Lin, W., Chang, R.-E., Hsieh, C.-J., Yaung, C.-L., and Chiang, T.-L., Development of a risk-adjusted capitation model based on principal inpatient diagnoses in Taiwan. Journal of the Formosan Medical Association 102(9):637–643, 2003.

    PubMed  Google Scholar 

  32. Pannarunothai, S., and Phanthunane, P., Using utilisation data to estimate future demand for medical internists: The impact of demographic demand driver in Thailand. Studies in Health Technology and Informatics 178:169–174, 2012.

    PubMed  Google Scholar 

  33. Stearns, S. C., Kovar, M. G., Hayes, K., and Koch, G. G., Risk indicators for hospitalization during the last year of life. Health Services Research 31(1):49–69, 1996.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Ministy of Health and Welfare. (2015). Ministy of Health and Welfare, National Health Insurance Administration. Retrieved from Ministy of health and welfare, national health insurance administration: http://www.nhi.gov.tw/webdata/webdata.aspx?menu=18&menu_id=683&webdata_id=444

  35. American Cancer Society, Global Cancer facts and figures. 2nd edition. Atlanta: American Cancer Society, 2011.

    Google Scholar 

  36. Wang, K. J., Makond, B., and Wang, K.-M., Modeling and predicting the occurrence of brain metastasis from lung Cancer by Bayesian network: A case study of Taiwan. Computers in Biology and Medicine 47:147–160, 2014.

    Article  PubMed  Google Scholar 

  37. Liu, C.-Y., Hung, Y.-T., Chuang, Y.-L., Chen, Y.-J., Weng, W.-S., Liu, J.-S., and Liang, K.-Y., Incorporating development stratification of Taiwan townships into sampling Design of Large Scale Health Interview Survey. Journal of Health Management 4(1):1–22, 2006.

    CAS  Google Scholar 

  38. Corral, J., Espinàs, J. A., Cots, F., Pareja, L., Solà, J., Font, R., and Borràs, J. M., Estimation of lung Cancer diagnosis and treatment costs based on a patient-level analysis in Catalonia (Spain). BMC Health Services Research 15(70):1–10, 2015.

    Google Scholar 

  39. Cucciare, M. A., and O’Donohue, W., Predicting future healthcare cost: How well does risk-adjustment work? Journal of Health Organization and Management 20(2):150–162, 2006.

    Article  PubMed  Google Scholar 

  40. Cartwright, N., What are randomised controlled trials good for? Philosophical studies 147(1):59, 2010.

    Article  Google Scholar 

  41. Kovesdy, C. P., and Kalantar-Zadeh, K., Observational studies versus randomized controlled trials: Avenues to causal inference in nephrology. Advances in Chronic kKidney Disease 19(1):11–18, 2012.

    Article  Google Scholar 

  42. 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.

  43. Scutari, M., Learning Bayesian networks with the bnlearn R package. Journal of Statistical Software 35(3):1–22, 2010.

    Article  Google Scholar 

  44. Scutari, M. (2015). Retrieved from bnlearn: Bayesian network structure learning, Parameter Learning and Inference: http://www.bnlearn.com/

  45. Chang, H.-Y., Evaluation of alternative diagnosis-based risk adjustment models and morbidity trajectories for application in Taiwan. Baltimore: The Johns Hopkins University, 2009.

    Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the comments and suggestions of the editor and the anonymous referees.

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Authors and Affiliations

  1. Department of Industrial Management, National Taiwan University of Science and Technology, No.43, Sec. 4, Keelung Rd., Da’an Dist., Taipei, 106, Taiwan, People’s Republic of China

    Kung-Jeng Wang & Jyun-Lin Chen

  2. CTBC Financial Management College, No. 600, Sec. 3, Taijiang Blvd., Annan District, Tainan City, 709, Taiwan, People’s Republic of China

    Kun-Huang Chen

  3. Department of Surgery, Shin-Kong Wu Ho-Su Memorial Hospital, Shilin District, Taipei, 111, Taiwan, People’s Republic of China

    Kung-Min Wang

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  1. Kung-Jeng Wang
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Correspondence to Kung-Jeng Wang.

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This work is partially supported by Ministry of Science & Technology of the Republic of China (Taiwan) under the grant no. MOST 105–2221-E-011-106 -MY2.

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Wang, KJ., Chen, JL., Chen, KH. et al. Survivability Prognosis for Lung Cancer Patients at Different Severity Stages by a Risk Factor-Based Bayesian Network Modeling. J Med Syst 44, 65 (2020). https://doi.org/10.1007/s10916-020-1537-5

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