Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Recommendation of Personalized Surveillance Interval of Colonoscopy via Survival Analysis

생존분석을 이용한 맞춤형 대장내시경 검진주기 추천

  • Gu, Jayeon (Department of Industrial Management Engineering, Korea University) ;
  • Kim, Eun Sun (Gastroenterology, Korea University College of Medicine) ;
  • Kim, Seoung Bum (Department of Industrial Management Engineering, Korea University)
  • 구자연 (고려대학교 산업경영공학과) ;
  • 김은선 (고려대학교 의과대학 내과학교실) ;
  • 김성범 (고려대학교 산업경영공학과)
  • Received : 2015.08.28
  • Accepted : 2015.11.23
  • Published : 2016.04.15
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Abstract

A colonoscopy is important because it detects the presence of polyps in the colon that can lead to colon cancer. How often one needs to repeat a colonoscopy may depend on various factors. The main purpose of this study is to determine personalized surveillance interval of colonoscopy based on characteristics of patients including their clinical information. The clustering analysis using a partitioning around medoids algorithm was conducted on 625 patients who had a medical examination at Korea University Anam Hospital and found several subgroups of patients. For each cluster, we then performed survival analysis that provides the probability of having polyps according to the number of days until next visit. The results of survival analysis indicated that different survival distributions exist among different patients' groups. We believe that the procedure proposed in this study can provide the patients with personalized medical information about how often they need to repeat a colonoscopy.

Keywords

References

  1. Bhambhri, A. (2011), Smarter Analytics for Big Data, IBM.
  2. Banez, L. L., Prasanna, P., Sun, L., Ali, A., Zou, Z., Adam, B. L., and Srivastava, S. (2003), Diagnostic potential of serum proteomic patterns in prostate cancer, The Journal of urology, 170(2), 442-446. https://doi.org/10.1097/01.ju.0000069431.95404.56
  3. Bender, M., Klein, R., Disch, A., and Ebert, A. (2000), A functional framework for web-based information visualization systems, Visualization and Computer Graphics, IEEE Transactions, 6(1), 8-23. https://doi.org/10.1109/2945.841118
  4. Berry, M. J. and Linoff, G. (1997), Data mining techniques : for marketing, sales, and customer support, John Wiley and Sons, Inc.
  5. Borg, I. and Groenen, P. J. (2005), Modern multidimensional scaling : Theory and applications, Springer Science and Business Media.
  6. Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A. (1984), Classification and regression trees, CRC press.
  7. Burroni, M., Corona, R., Dell'Eva, G., Sera, F., Bono, R., Puddu, P., and Rubegni, P. (2004), Melanoma computer-aided diagnosis reliability and feasibility study, Clinical cancer research, 10(6), 1881-1886. https://doi.org/10.1158/1078-0432.CCR-03-0039
  8. Chen, M.Y. (2002), Survival duration of plants : evidence from the US petroleum refining industry, International Journal of Industrial Organization, 20(4), 517-555. https://doi.org/10.1016/S0167-7187(00)00106-5
  9. Cho, I. S. and Chung, E. (2011), Predictive bayesian network model using electronic patient records for prevention of hospital-acquired pressure ulcers, Journal of Korean Academy of Nursing, 41(3), 423-431. https://doi.org/10.4040/jkan.2011.41.3.423
  10. Choi, J., Han, S., Kang, H., and Kim, E. (1998), Data mining decision tree analysis using answer tree, SPSS Academy, 17-23.
  11. Christodoulou, C. and Pattichis, C. S. (1999), Unsupervised pattern recognition for the classification of EMG signals, Biomedical Engineering, IEEE Transactions, 46(2), 169-178. https://doi.org/10.1109/10.740879
  12. Curram, S. P. and Mingers, J. (1994), Neural networks, decision tree induction and discriminant analysis : An empirical comparison, Journal of the Operational Research Society, 45(4), 440-450. https://doi.org/10.1057/jors.1994.62
  13. Goldman, L., Cook, E. F., Brand, D. A., Lee, T. H., Rouan, G. W., Weisberg, M. C., and Jakubowski, R. (1988), A computer protocol to predict myocardial infarction in emergency department patients with chest pain, New England Journal of Medicine, 318(13), 797-803. https://doi.org/10.1056/NEJM198803313181301
  14. Gorden, A. D. (1999), Classification, Chapman and Hall/CRC.
  15. Gower, J. C. (1971), A general coefficient of similarity and some of its properties, Biometrics, 27(4), 857-871. https://doi.org/10.2307/2528823
  16. Hastie, T., Friedman, J., and Tibshirani, R. (2001), The elements of statistical learning, Springer.
  17. Han, P. and Baek, J. G. (2014), Prediction model on delivery time in display FAB using survival analysis, Journal of the Korea Institute of Institute of Industrial Engineers, 40(3), 283-290. https://doi.org/10.7232/JKIIE.2014.40.3.283
  18. Hartigan, J. A. (1975), Clustering algorithms, John Wiley and Sons, Inc.
  19. Hong, S. N., Yang, D. H., Kim, Y. H., Hong, S. P., Shin, S. J., Kim, S. E., and Yang, S. K. (2012), Korean guidelines for post-polypectomycolonoscopic surveillance, The Korean Journal of Gastroenterology, 59(2), 99-117. https://doi.org/10.4166/kjg.2012.59.2.99
  20. Hosmer Jr, D. W., Lemeshow, S., and May, S. (2011), Applied survivalanalysis : regression modeling of time to event data, Wiley.com.
  21. Jo, I. and Kim, J. (2011), Trend research-based clinical decision support systems based on Electronic Health Records, Communications of the Korean Institute of Information Scientists and Engineers, 29(2), 92-100.
  22. Joo, S., Yang, Y. S., Moon, W. K., and Kim, H. C. (2004), Computer-aided diagnosis of solid breast nodules : use of an artificial neural network based on multiple sonographic features, Medical Imaging, IEEE Transactions, 23(10), 1292-1300. https://doi.org/10.1109/TMI.2004.834617
  23. Jung, K. W., Won, Y. J., Kong, H. J., Oh, C. M., Cho, H., Lee, D. H., and Lee, K. H. (2015), Cancer statistics in Korea : incidence, mortality, survival, and prevalence in 2012, Cancer research and treatment : official journal of Korean Cancer Association, 47(2), 127. https://doi.org/10.4143/crt.2015.060
  24. Kalbfleisch, J. D. and Prentice, R. L. (2011), The statistical analysis of failure time data, John Wiley and Sons.
  25. Kaplan, E. L. and Meier, P. (1958), Nonparametric estimation from incomplete observations, Journal of the American statistical association, 53(282), 457-481. https://doi.org/10.1080/01621459.1958.10501452
  26. Kaufman, L. and Rousseeuw, P. J. (2009), Finding groups in data : an introduction to cluster analysis, John Wiley and Sons.
  27. Lee, B. and Jung, S. (2002) Korean National Guidelines on Screening and Surveillance for Early Detection of Colorectal Cancers (KSCP and NCC), Korean Society of Gastointestinal Endoscopy, 45(8), 981-891.
  28. Lee, Y. (2010), Study on Prediction Model of insolvent companies using survival analysis techniques Guarantee, Korean Market Economy Research, 39(3), 1-24.
  29. Lieberman, D. A., Rex, D. K., Winawer, S. J., Giardiello, F. M., Johnson, D. A., and Levin, T. R. (2012), Guidelines for colonoscopy surveillance after screening and polypectomy : a consensus update by the US Multi-Society Task Force on Colorectal Cancer, Gastroenterology, 143(3), 844-857. https://doi.org/10.1053/j.gastro.2012.06.001
  30. Mantel, N. (1966), Evaluation of survival data and two new rank order statistics arising in its consideration, Cancer chemotherapy reports, 50(3), 163-170.
  31. National Cancer Center (2011), National Cancer Control Project, Available at : http://www.ncc.re.kr/manage/manage12_00.jsp.
  32. Patil, N. N., Mottrie, A., Sundaram, B., and Patel, V. R. (2008), Robotic-assisted laparoscopic ureteral reimplantation with psoas hitch: a multi-institutional, multinational evaluation, Urology, 72(1), 47-50. https://doi.org/10.1016/j.urology.2007.12.097
  33. Perou, C. M., Sorlie, T., Eisen, M. B., van de Rijn, M., Jeffrey, S. S., Rees, C. A., and Botstein, D. (2000), Molecular portraits of human breast tumours, Nature, 406(6797), 747-752. https://doi.org/10.1038/35021093
  34. Ries, L. A. G., Melbert, D., and Krapcho, M. (2007), SEER Cancer Statistics Review, 1975-2004, Bethesda, MD: National Cancer Institute, based on November 2006 SEER data submission, posted to the SEER Web site.
  35. Rousseeuw, P. J. (1987), Silhouettes : a graphical aid to the interpretation and validation of cluster analysis, Journal of computational and applied mathematics, 20, 53-65. https://doi.org/10.1016/0377-0427(87)90125-7
  36. South Korea Statistics (2013), Year mortality statistics.
  37. Strober, M., Freeman, R., and Morrell, W. (1997), The long-term course of severe anorexia nervosa in adolescents : Survival analysis of recovery, relapse, and outcome predictors over 10-5 years in a prospective study, International Journal of Eating Disorders, 22(4), 339-360. https://doi.org/10.1002/(SICI)1098-108X(199712)22:4<339::AID-EAT1>3.0.CO;2-N
  38. Thiis-Evensen, E., Hoff, G. S., Sauar, J., Langmark, F., Majak, B. M., and Vatn, M. H. (1999), Population-based surveillance by colonoscopy : effect on the incidence of colorectal cancer : Telemark Polyp Study I, Scandinavian journal of gastroenterology, 34(4), 414-420. https://doi.org/10.1080/003655299750026443
  39. Winawer, S. J., Zauber, A. G., Ho, M. N., O'Brien, M. J., Gottlieb, L. S., Sternberg, S. S., and Stewart, E. T. (1993), Prevention of colorectal cancer by colonoscopic polypectomy, New England Journal of Medicine, 329(27), 1977-1981. https://doi.org/10.1056/NEJM199312303292701
  40. Ziegel, E. R. (1997), Survival analysis using the SAS system, Technometrics, 39(3), 344.