Commenced in January 2007
Paper Count: 30075
Concepts Extraction from Discharge Notes using Association Rule Mining
Authors: Basak Oguz Yolcular
Abstract: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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071326Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1233
 M. Konchady , Text Mining Application Programming. Boston: Charles River Media, 2006, ch. 1.
 D.B. Johnson, R.K. Taira, A.F. Cardenas, and D.R. Aberle, "Extracting Information from Free Text Radiology Reports", Int. J. Digit Libr., vol. 1, no. 3, pp. 297-308, Dec. 1997.
 G. Schadow , C.J. Mcdonald,. "Extracting Structured Information from Free Text Pathology Reports," in Conf. 2003 AMIA Annu. Symp. Proc., pp. 584-8.
 R.A. Erhardt, R. Schneider , and C. Blaschke, "Status of Text Mining Techniques Applied to Biomedical Text," Drug Dicovery Today, vol. 11, no. 7-8, pp. 315-25, Apr. 2006.
 A.M. Cohen, W.R. Hersh, "A Survey of Current Work in Biomedical Text Mining," Briefings in Bioinformatics, vol. 6, no. 1, pp. 57-71, Mar. 2005.
 Wikipedia, "Otolaryngology (Unpublished work style)," unpublished.
 Google, "Zemberek (Unpublished work style)," unpublished.
 DB2 Universal Database, "Associations (Unpublished work style)," unpublished.
 S.E. Brossette, A.P. Sprague, J.M. Hardin, K.W.T. Jones, and S.A. Moser , "Association rules and data mining in hospital infection control and public health surveillance," Journal of American Medical Informatics Association, vol. 5, pp. 373-81, 1998.
 J. Paetz, R.W. Brause, "A frequent patterns tree approach for rule generation with categorical septic shock patient data," in Proceedings of the second international symposium on medical data analysis, London: Springer-Verlag, 2001, pp. 207-12.
 M. Ohsaki, Y. Sato, H. Yokoi, and T. Yamaguchi, "A rule discovery support system for sequential medical data in the case study of a chronic hepatitis dataset," in Proceedings of the ECML/PKDD 2003 discovery challenge workshop.
 J. Chen, H. He, G.J. Williams, and Jin H, "Temporal sequence associations for rare events," in Advances in knowledge discovery and data mining, Berlin/Heidelberg: Springer, 2004, pp. 235-9.
 C. Ordonez, N.F. Ezquerra, and C.A. Santana, "Constraining and summarizing association rules in medical data," Knowledge and Information Systems,vol. 3, pp. 1-2, 2006.
 R. Agrawal, T. Imielinski, and A. Swami, "Mining association rules between sets of items in large databases," in Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC: SIGMOD Conference, 1993, pp. 207-216.
 T. Scheffer, "Finding Association Rules That Trade Support Optimally against Confidence," in Proc of the 5th European Conf. on principles and Practice of Knowledge Discovery in Databases (PKDD'01), Freiburg, Germany: Springer-Verlag, 2001, pp. 424-435.
 I.H. Witten, E. Frank, "Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations," San Francisco, 2005.
 E. Frank, M. Hall, L. Trigg, G. Holmes, and I.H. Witten, "Data Mining in Bioinformatics using Weka," Bioinformatics, vol. 20, no. 15, pp. 2479-2481, 2004.