DISEArch - A Strategy for Searching Electronic Medical Health Records

David Elias Peña Clavijo, Alexandra Pomares Quimbaya, Rafael A. Gonzalez


This paper proposes DISEArch, a novel strategy for searching electronic health records (EHR) of patients that have a specific disease. The objective of DISEArch is to enhance research activities on disease analysis allowing researchers to describe the disease they are interested on, and providing them the EHRs that best match their description. Its principle is to improve the precision of searching EHRs combining the analysis of structured attributes with the analysis of narrative text attributes producing a semantic ranking of EHRs with respect to a given disease. DISEArch is useful in medical systems where the information about the primary diagnosis of patients may be hidden in narrative text hindering the automatic detection of relevant records for clinical studies.


  1. Antal, P., de Moor, B., and Mészáros, T. (2001). Annotated bayesian networks: A tool to integrate textual and probabilistic medical knowledge. In Proc. of the 14th IEEE Symp. on Computer-Based Medical Systems, CBMS 7801.
  2. Averbuch, M., Karson, T. H., Ben-Ami, O., and Rokach, L. (2004). Context-sensitive medical information retrieval. Studies in health technology and informatics.
  3. Bechhofer, S., van Harmelen, F., Hendler, J., and Horrocks, I. (2009). ”owl web ontology language reference”. Technical report, W3C.
  4. Breault, J. L., Goodall, C. R., and Fos, P. J. (2002). Data mining a diabetic data warehouse. Artificial Intelligence in Medicine.
  5. Chapman, W. W., Bridewell, W., Hanbury, P., and Cooper (2001). A simple algorithm for identifying negated findings and diseases in discharge summaries. J. of Biomedical Informatics.
  6. Claster, W., Shanmuganathan, S., and Ghotbi, N. (2008). Text mining of medical records for radiodiagnostic decision-making. JCP.
  7. Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V., and Aswani, N. (2011). Text Processing with GATE (Version 6).
  8. Ginter, F., Suominen, H., Pyysalo, S., and Salakoski, T. (2009). Combining hidden markov models and latent semantic analysis for topic segmentation and labeling: Method and clinical application. I. J. Medical Informatics.
  9. Han, H., Choi, Y., Choi, Y. M., Zhou, X., and Brooks, A. D. (2006). A generic framework: From clinical notes to electronic medical records. Computer-Based Medical Systems, IEEE Symp.
  10. Hanauer, D. A. (2006). Emerse: The electronic medical record search engine. AMIA A. Symp Proc.
  11. Hotho, A., Nürnberger, A., and Paass, G. (2005). A brief survey of text mining. LDV Forum.
  12. Huang, M.-J., Chen, M.-Y., and Lee (2007). Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis. Expert Syst. Appl.
  13. Keeney, R. and Raiffa, H. (1976). Decisions with multiple objectives: Preferences and value tradeoffs. J. Wiley, New York.
  14. Manning, C. D., Raghavan, P., and Schtze, H. (2008). Introduction to Information Retrieval.
  15. Rokach, L., Romano, R., and Maimon, O. (2008). Negation recognition in medical narrative reports. I. R.
  16. Schmid, H. (1994). Probabilistic part-of-speech tagging using decision trees. In Proceedings of the International Conference on New Methods in Language Processing.
  17. Seyfried, L., Hanauer, D. A., Nease, D., and Albeiruti (2009). Enhanced identification of eligibility for depression research using an electronic medical record search engine. Inter. J. of Medical Informatics.
  18. Spasic, I., Ananiadou, S., McNaught, J., and Kumar, A. (2005). Text mining and ontologies in biomedicine: Making sense of raw text. Briefings in Bioinformatics.
  19. USNLM (2011). Unified medical language system R (umls R ). http://www.nlm.nih.gov/research/umls/. Noviembre 25, 2011.
  20. Zhou, X., Han, H., Chankai, I., Prestrud, A. A., and Brooks, A. D. (2005). Converting semi-structured clinical medical records into information and knowledge. In Proc. of the 21st Inter. C. on Data Eng. WS.

Paper Citation

in Harvard Style

Elias Peña Clavijo D., Pomares Quimbaya A. and A. Gonzalez R. (2012). DISEArch - A Strategy for Searching Electronic Medical Health Records . In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8565-10-5, pages 151-156. DOI: 10.5220/0004004601510156

in Bibtex Style

author={David Elias Peña Clavijo and Alexandra Pomares Quimbaya and Rafael A. Gonzalez},
title={DISEArch - A Strategy for Searching Electronic Medical Health Records},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},

in EndNote Style

JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - DISEArch - A Strategy for Searching Electronic Medical Health Records
SN - 978-989-8565-10-5
AU - Elias Peña Clavijo D.
AU - Pomares Quimbaya A.
AU - A. Gonzalez R.
PY - 2012
SP - 151
EP - 156
DO - 10.5220/0004004601510156