Summary
Clinical databases store large amounts of information about patients and their medical conditions. Data mining techniques can extract relationships and patterns implicit in this wealth of data, and thus be helpful in understanding the progression of diseases and the efficacy of the associated therapies. In this perspective, in Pisa (Italy) we have started an important data collection and analysis project, where a very large number of epidemiological, clinical, immunological and genetic variables collected before the transplantation of a solid organ, and during the follow-up assessment of the patients, are stored in a datawarehouse for future mining. This on-going data collection involves all liver, kidney, pancreas and kidney-pancreas transplantations of the last five years of one of the largest (as to number of transplantations) centers in Europe. The project ambitious goal is to gain deeper insights in all the phenomena related to solid organ transplantation, with the aim of improving the donor-recipient matching policy used nowadays. In this chapter we report in details two different data mining activities developed within this project. The first analysis involves mining genetic data of patients affected by terminal hepatic cirrhosis with viral origin (HCV and HBV) and patients with terminal hepatic cirrhosis with non-viral origin (autoimmune): the goal is to assess the influence of the HLA antigens on the course of the disease. In particular, we have evaluated if some genetic configurations of the class I and class II HLA are significantly associated with the triggering causes of the hepatic cirrhosis. The second analysis involves clinical data of a set of patients in the follow-up of a liver transplantation. The aim of the data analysis is that of assessing the effectiveness of the extracorporeal photopheresis (ECP) as a therapy to prevent rejection in solid organ transplantation. For both analyses we describe in details, the medical context and goal, the nature and structure of the data. We also discuss which kind of data mining technique is the most suitable for our purposes, and we describe the details of the knowledge discovery process followed and extracted knowledge.
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References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings ACM SIGMOD (1993)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the 20th VLDB (1994)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Yu, P.S., Chen, A.S.P. (eds.) Eleventh International Conference on Data Engineering, Taipei, Taiwan, pp. 3–14. IEEE Computer Society Press, Los Alamitos (1995)
Bretagne, S., Vidaud, M., Kuentz, M., Cordonnier, C., Henni, T., Vinci, G., Goossens, M., Vernant, J.: Mixed blood chimerism in t cell-depleted bone marrow transplant recipients: evaluation using dna polymorphisms. Circulation Research 70, 1692–1695 (1987)
Clark, A.: Inference of haplotypes from pcr-amplified samples of diploid populations. In: Molecular Biology and Evolution, pp. 111–122 (1990)
Edelson, R., Berger, C., Gasparro, F., Jegasothy, B., Heald, P., Wintroub, B., Vonderheid, E., Knobler, R., Wolff, K., Plewig, G.: Treatment of cutaneous t-cell lymphoma by extracorporeal photochemotherapy. N. Engl. J. Med. 316, 297–303 (1987)
Giannotti, F., Nanni, M., Pedreschi, D.: Efficient mining of temporally annotated sequences. In: Proceedings of the Sixth SIAM International Conference on Data Mining (2006)
Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F.: Mining sequences with temporal annotations. In: Proceedings of the 2006 ACM Symposium on Applied Computing (SAC), pp. 593–597 (2006)
Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F.: Trajectory patter mining. In: The Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2007)
Gusfield, D.: A practical algorithm for deducing haplotypes in diploid populations. In: Press, A. (ed.) Proceedings of the Eigth International Conference on Intelligent Systems in Molecular Biology, pp. 915–928 (2000)
Gusfield, D.: A practical algorithm for optimal inference of haplotypes from diploid populations. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pp. 183–189. AAAI Press, Menlo Park (2000)
Gusfield, D.: Inference of haplotypes from samples of diploid populations: complexity and algorithms. J. of Computational Biology 8(3) (2001)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of ACM SIGMOD (2000)
Khuu, H., Desmond, R., Huang, S., Marques, M.: Characteristics of photopheresis treatments for the management of rejection in heart and lung transplant recipients. J. Clin. Apher. 17(1), 27–32 (2002)
Kim, I., Moon, S.-O., Park, S.K., Chae, S.W., Koh, G.Y.: Angiopoietin-1 reduces vegf-stimulated leukocyte adhesion to endothelial cells by reducing icam-1, vcam-1, and e-selectin expression. Circulation Research 89, 477–481 (2001)
Knobler, R., Graninger, W., Lindmaier, A., Trautinger, F.: Photopheresis for the treatment of lupus erythematosus. Ann. NY Acad. Sci. 636, 340–356 (1991)
Kobayashi, T., Yokoyama, I., Hayashi, S., Negita, M., Namii, Y., Nagasaka, T., Ogawa, H., Haba, T., Tominaga, Y., Takagi, K.U.H.: Genetic polymorphism in the il-10 promoter region in renal transplantation. Transplant Proc. 31, 755–756 (1999)
Lehrer, M., Ruchelli, E., Olthoff, K., French, L., Rook, A.: Successful reversal of recalcitrant hepatic allograft rejection by photopheresis. Liver Transpl. 6(5), 644–647 (2000)
Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proceedings of the 2001 IEEE International Conference on Data Mining, ICDM 2001 (2001)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: 4th Int. Conf. Knowledge Discovery and Data Mining (KDD 1998), New York, pp. 80–86 (1998)
Lucchese, C., Orlando, S., Perego, R.: Dci closed: A fast and memory efficient algorithm to mine frequent closed itemsets. In: FIMI (2004)
Marroni, F., Curcio, M., Fornaciari, S., Lapi, S., Mariotti, M., Scatena, F., Presciuttini, S.: Microgeographic variation of hla-a, -b, and -dr haplotype frequencies in tuscany, italy: implications for recruitment of bone marrow donors. Tissue Antigens 64, 478–485 (2004)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1998)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: Prefixspan: Mining sequential patterns by prefix-projected growth. In: Proceedings of the 17th International Conference on Data Engineering, pp. 215–224 (2001)
Pei, J., Zhang, X., Cho, M., Wang, H., Yu, P.: Maple: A fast algorithm for maximal pattern-based clustering. In: Proceedings of the Third IEEE International Conference on Data Mining, ICDM 2003 (2003)
Thursz, R., Kwiatowski, D., Allsopp, C., Greenwood, B., Thomas, H., Hill, A.: Association between a mhc class ii allele and clerance of hepatitis b virus in gambia. New England Journal of Medicine 332, 1065–1069 (1995)
Thursz, R., Yallop, R., Goldins, R., Trepo, C., Thomas, H.: Influence of mhc class ii genotype on outcome of infection with hepatitis c virus. Lancet. 354, 2119–2124 (1999)
Urbani, L., Mazzoni, A., Catalano, G., Simone, P.D., Vanacore, R., Pardi, C., Bortoli, M., Biancofiore, G., Campani, D., Perrone, V., Mosca, F., Scatena, F., Filipponi, F.: The use of extracorporeal photopheresis for allograft rejection in liver transplant recipients. Transplant Proc. 36(10), 3068–3070 (2004)
Verity, D., Marr, J., Ohno, S.: Behçet’s disease, the silk road and hla-b51: historical and geographical perspectives. Tissue Antigens 54, 213–220 (1999)
Verity, D., Wallace, G., Delamaine, L.: Mica allele profiles and hla class i associations in behçet’s disease. Immunogenetics 49, 613–617 (1999)
Wang, J., Han, J., Pei, J.: Closet+: searching for the best strategies for mining frequent closed itemsets. In: KDD 2003: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 236–245. ACM Press, New York (2003)
Yiu, M.L., Mamoulis, N.: Frequent-pattern based iterative projected clustering. In: Proceedings of the Third IEEE International Conference on Data Mining, ICDM 2003 (2003)
Zaki, M.J.: SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 42(1/2), 31–60 (2001)
Zaki, M.J., Hsiao, C.-J.: Charm: An efficient algorithm for closed itemset mining. In: SDM (2002)
Zhao, Q., Bhowmick, S.: Sequential pattern mining: a survey. Technical Report. Center for Advanced Information Systems, School of Computer Engineering, Nanyang Technological University, Singapore (2003)
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Berlingerio, M., Bonchi, F., Curcio, M., Giannotti, F., Turini, F. (2009). Mining Clinical, Immunological, and Genetic Data of Solid Organ Transplantation. In: Sidhu, A.S., Dillon, T.S. (eds) Biomedical Data and Applications. Studies in Computational Intelligence, vol 224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02193-0_9
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