참고문헌
- Ahn, J. H. (2013). Nomogram for prediction of prostate cancer in Korean men with serum prostate-specic antigen less than 10ng/mL, Busan University, Busan.
- Cook, N. R. (2008). Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve, Clinical Chemistry, 54, 17-23.
- D'Agostino, R. B., Grundy, S., Sullivan, L. M., and Wilson, P. (2001). Validation of the Framingham coronary heart disease prediction scores, Journal of the American Medical Association, 286, 180-187. https://doi.org/10.1001/jama.286.2.180
- Heo, M. H. and Lee, Y. G. (2008). Data Mining Modeling and Example, Hannarae, Seoul.
- Iasonos, A., Schrag, D., Raj, G. V., and Panageas, K. S. (2008). How to build and interpret a nomogram for cancer prognosis, Journal of Clinical Oncology, 26, 1364-1370. https://doi.org/10.1200/JCO.2007.12.9791
- Jun, H. J. (2015). Establishment of a nomogram to predict the prognosis of metastatic or recurrent gastric cancer patients (master's thesis), Yonsei University, Seoul.
- Jung, Y. M and Lee, H. Y. (2011). Chronic obstructive pulmonary disease in Korea: prevalence, risk factors, and quality of life, Journal of Korean Academy of Nursing, 41, 149-156. https://doi.org/10.4040/jkan.2011.41.2.149
- Kim, S. H., Shin, K. H., Kim, H. Y., Cho, Y. J., Noh, J. K., Suh, J. S., and Yang, W. I. (2014). Postoperative nomogram to predict the probability of metastasis in Enneking stage IIB extremity osteosarcoma, BMC Cancer, 14, 666. https://doi.org/10.1186/1471-2407-14-666
- Korea Centers for Disease Control and Prevention (2013-2015). The Sixth National Health and Nutrition Examination Survey (KNHANES VI), from: http://knhanes.cdc.go.kr/
- Korea Centers for Disease Control and Prevention (2016). Korea Health Statistics 2015 : Korea National Health and Nutrition Examination Survey (KNHANES VI-3), Cheongju, from: https://knhanes.cdc.go.kr/knhanes/sub04/sub04 03.do? bclassType=7
- Korean Statistical Information Service (2015). Cause of Death, from: http://kosis.kr/statHtml/statHtml.do? orgId=101&tblId=DT 1B34E01&connpath=I2
- Kyung, S. Y., Kim, Y. S., Kim, W. J., Park, M. S., Song, J. W., Yum, H. K., Yoon, H. K., Rhee, C. K., and Jeong, S. H. (2015). Guideline for the prevention and management of particulate matter/Asian dust particle induced adverse health effect on patients with pulmonary diseases, Journal of the Korean Medical Association, 58, 1060-1069. https://doi.org/10.5124/jkma.2015.58.11.1060
- Lee, J. W., Park, M. R., and Yu, H. N. (2005). Statistical Method for Bioscience Research, Freedom Academy, Seoul.
- Lee, S. C. and Chang, M. C. (2014). Development and validation of web-based nomogram to predict postoperative invasive component in ductal carcinoma in situ at core needle breast biopsy, Healthcare Informatics Research, 20, 152-156. https://doi.org/10.4258/hir.2014.20.2.152
- Mannino, D. M. (2007). Global burden of COPD: risk factors, prevalence, and future trends, The Lancet, 370, 765-773. https://doi.org/10.1016/S0140-6736(07)61380-4
- Mozina, M., Demsar, J., Smrke, D., and Zupan, B. (2004). Nomograms for Naive Bayesian classifiers and how can they help in medical data analysis, MEDINFO 2004, 1762, 765-773.
- Nam, B. H. and D'Agostino, R. B. (2002). Discrimination index, the area under the ROC curve, In Goodness- of-Fit Tests and Model Validity, 267-279.
- Park, H. Y., Jung, S. Y., Lee, K. H., Bae, W. K., Lee, K. H., Han, J. S., Kim, S. R., Choo, S. Y., Jeong, J. M., Kim, H. R., Ro, H. J. and Jeong, H. S. (2015). Prevalence of chronic obstructive lung disease in Korea using data from the fifth Korea National Health and Nutrition Examination Survey, Korean Journal of Family Medicine, 36, 128-134. https://doi.org/10.4082/kjfm.2015.36.3.128
- Yang, D. (2014). Build prognostic nomograms for risk assessment using SAS. In Proceedings of SAS Global Forum 2013, from: http://support.sas.com/resources/papers/proceedings13/264-2013.pdf.
- Zinlinsky, J. and Bednarek, M. (2001). Early detection of COPD in a high-risk population using spirometric screening, Chest, 119, 731-736. https://doi.org/10.1378/chest.119.3.731