Obstetric Medical Record Processing and Information Retrieval

  • Miroslav Bursa
  • Lenka Lhotska
  • Vaclav Chudacek
  • Michal Huptych
  • Jiri Spilka
  • Petr Janku
  • Martin Huser
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 91)

Abstract

This paper describes the process of mining information from loosely structured medical textual records with no apriori knowledge. In the paper we depict the process of mining a large dataset of ~50,000–120,000 records × 20 attributes in database tables, originating from the hospital information system (thanks go to the University Hospital in Brno, Czech Republic) recording over 10 years. This paper concerns only textual attributes with free text input, that means 613,000 text fields in 16 attributes. Each attribute item contains ~800–1,500 characters (diagnoses, medications, etc.). The output of this task is a set of ordered/nominal attributes suitable for rule discovery mining and automated processing.

Information mining from textual data becomes a very challenging task when the structure of the text record is very loose without any rules. The task becomes even more difficult when natural language is used and no apriori knowledge is available. The medical environment itself is also very specific: the natural language used in textual description varies with the personality creating the record (there are many personalized approaches), however it is restricted by terminology (i.e. medical terms, medical standards, etc.). Moreover, the typical patient record is filled with typographical errors, duplicates, ambiguities and many (nonstandard) abbreviations.

Note that this project is an ongoing process (and research) and new data are irregularly received from the medical facility, justifying the need for robust and fool-proof algorithms.

Keywords

Swarm Intelligence Ant Colony Textual Data Mining Medical Record Processing Hospital Information System 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Freitag, D., McCallum, A.K.: Information extraction with hmms and shrinkage. In: Proceedings of the AAAI Workshop on Machine Learining for Information Extraction (1999)Google Scholar
  2. 2.
    Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the ICML, pp. 282–289 (2001); Text processing: interobserver agreement among linquists at 70Google Scholar
  3. 3.
    Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)MATHGoogle Scholar
  4. 4.
    Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chretien, L.: The dynamics of collective sorting robot-like ants and ant-like robots. In: Proceedings of the First International Conference on Simulation of Adaptive Behavior on From Animals to Animats, pp. 356–363. MIT Press, Cambridge (1990)Google Scholar
  5. 5.
    Lumer, E.D., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: From Animals to Animats: Proceedings of the 3th International Conference on the Simulation of Adaptive Behaviour, vol. 3, pp. 501–508 (1994)Google Scholar
  6. 6.
    Vizine, A.L., de Castro, N.L., Hruschka, E.R., Gudwin, R.R.: Towards improving clustering ants: An adaptive ant clustering algorithm. Informatica 29, 143–154 (2005)MATHGoogle Scholar
  7. 7.
    Handl, J., Knowles, J., Dorigo, M.: Ant-based clustering and topographic mapping. Artificial Life 12(1), 35–61 (2006)CrossRefGoogle Scholar
  8. 8.
    Bursa, M., Lhotska, L., Macas, M.: Hybridized swarm metaheuristics for evolutionary random forest generation. In: Proceedings of the 7th International Conference on Hybrid Intelligent Systems 2007 (IEEE CSP), pp. 150–155 (2007)Google Scholar
  9. 9.
    Bursa, M., Lhotska, L.: Ant colony cooperative strategy in biomedical data clustering. SCI. Springer, Heidelberg (2007)Google Scholar
  10. 10.
    Bursa, M., Huptych, M., Lhotska, L.: Ant colony inspired metaheuristics in biological signal processing: Hybrid ant colony and evolutionary approach. In: Biosignals 2008-II, vol. 2, pp. 90–95. INSTICC Press, Setubal (2008)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Miroslav Bursa
    • 1
  • Lenka Lhotska
    • 1
  • Vaclav Chudacek
    • 1
  • Michal Huptych
    • 1
  • Jiri Spilka
    • 1
  • Petr Janku
    • 2
  • Martin Huser
    • 2
  1. 1.Department of Cybernetics, Faculty of Electrical EngineeringCzech Technical UniversityPragueCzech Republic
  2. 2.Obstetrics and Gynaecology clinicUniversity Hospital in BrnoCzech Republic

Personalised recommendations