A large amount of valuable information is available in plain text clinical reports. New techniques and technologies are applied to extract information from these reports. In this study, we developed a domain based software system to transform 600 Otorhinolaryngology discharge notes to a structured form for extracting clinical data from the discharge notes. In order to decrease the system process time discharge notes were transformed into a data table after preprocessing. Several word lists were constituted to identify common section in the discharge notes, including patient history, age, problems, and diagnosis etc. N-gram method was used for discovering terms co-Occurrences within each section. Using this method a dataset of concept candidates has been generated for the validation step, and then Predictive Apriori algorithm for Association Rule Mining (ARM) was applied to validate candidate concepts., {"references":["M. Konchady , Text Mining Application Programming. Boston: Charles\nRiver Media, 2006, ch. 1.","D.B. Johnson, R.K. Taira, A.F. Cardenas, and D.R. Aberle, \"Extracting\nInformation from Free Text Radiology Reports\", Int. J. Digit Libr., vol.\n1, no. 3, pp. 297-308, Dec. 1997.","G. Schadow , C.J. Mcdonald,. \"Extracting Structured Information from\nFree Text Pathology Reports,\" in Conf. 2003 AMIA Annu. Symp. Proc.,\npp. 584-8.","R.A. Erhardt, R. Schneider , and C. Blaschke, \"Status of Text Mining\nTechniques Applied to Biomedical Text,\" Drug Dicovery Today, vol.\n11, no. 7-8, pp. 315-25, Apr. 2006.","A.M. Cohen, W.R. Hersh, \"A Survey of Current Work in Biomedical\nText Mining,\" Briefings in Bioinformatics, vol. 6, no. 1, pp. 57-71, Mar.\n2005.","Wikipedia, \"Otolaryngology (Unpublished work style),\" unpublished.","Google, \"Zemberek (Unpublished work style),\" unpublished.","DB2 Universal Database, \"Associations (Unpublished work style),\"\nunpublished.","S.E. Brossette, A.P. Sprague, J.M. Hardin, K.W.T. Jones, and S.A.\nMoser , \"Association rules and data mining in hospital infection control\nand public health surveillance,\" Journal of American Medical\nInformatics Association, vol. 5, pp. 373-81, 1998.\n[10] J. Paetz, R.W. Brause, \"A frequent patterns tree approach for rule\ngeneration with categorical septic shock patient data,\" in Proceedings of\nthe second international symposium on medical data analysis, London:\nSpringer-Verlag, 2001, pp. 207-12.\n[11] M. Ohsaki, Y. Sato, H. Yokoi, and T. Yamaguchi, \"A rule discovery\nsupport system for sequential medical data in the case study of a chronic\nhepatitis dataset,\" in Proceedings of the ECML/PKDD 2003 discovery\nchallenge workshop.\n[12] J. Chen, H. He, G.J. Williams, and Jin H, \"Temporal sequence\nassociations for rare events,\" in Advances in knowledge discovery and\ndata mining, Berlin/Heidelberg: Springer, 2004, pp. 235-9.\n[13] C. Ordonez, N.F. Ezquerra, and C.A. Santana, \"Constraining and\nsummarizing association rules in medical data,\" Knowledge and\nInformation Systems,vol. 3, pp. 1-2, 2006.\n[14] R. Agrawal, T. Imielinski, and A. Swami, \"Mining association rules\nbetween sets of items in large databases,\" in Proceedings of the 1993\nACM SIGMOD International Conference on Management of Data,\nWashington, DC: SIGMOD Conference, 1993, pp. 207-216.\n[15] T. Scheffer, \"Finding Association Rules That Trade Support Optimally\nagainst Confidence,\" in Proc of the 5th European Conf. on principles\nand Practice of Knowledge Discovery in Databases (PKDD'01),\nFreiburg, Germany: Springer-Verlag, 2001, pp. 424-435.\n[16] I.H. Witten, E. Frank, \"Data Mining: Practical Machine Learning Tools\nand Techniques with Java Implementations,\" San Francisco, 2005.\n[17] E. Frank, M. Hall, L. Trigg, G. Holmes, and I.H. Witten, \"Data Mining\nin Bioinformatics using Weka,\" Bioinformatics, vol. 20, no. 15, pp.\n2479-2481, 2004."]}