• Hercules Dalianis
Open Access


This chapter gives a short introduction of the research area of clinical text mining.


  1. Allvin, H., Carlsson, E., Dalianis, H., Danielsson-Ojala, R., Daudaravicius, V., Hassel, M., et al. (2011). Characteristics of Finnish and Swedish intensive care nursing narratives: A comparative analysis to support the development of clinical language technologies. Journal of Biomedical Semantics, 2(Suppl 3), 1–11.CrossRefGoogle Scholar
  2. Cohen, K. B., & Demner-Fushman, D. (2014). Biomedical Natural Language Processing (Vol. 11). Amsterdam: John Benjamins Publishing Company.CrossRefGoogle Scholar
  3. Dalianis, H. (2014). Clinical text retrieval - An overview of basic building blocks and applications. In Professional Search in the Modern World (pp. 147–165). Berlin: Springer.Google Scholar
  4. Dalianis, H., Hassel, M., & Velupillai, S. (2009). The Stockholm EPR Corpus-characteristics and some initial findings. In Proceedings of ISHIMR 2009, Evaluation and Implementation of e-Health and Health Information Initiatives: International Perspectives. 14th International Symposium for Health Information Management Research (pp. 243–249).Google Scholar
  5. Ducel, G., Fabry, J., & Nicolle, L. (Eds.). (2002). Prevention of Hospital Acquired Infections: A Practical Guide., 2nd edn. World Health Organization. Accessed 11 Jan 2018.
  6. Freeman, R., Moore, L. S. P., Álvarez, L. G., Charlett, A., & Holmes, A. (2013). Advances in electronic surveillance for healthcare-associated infections in the 21st century: A systematic review. Journal of Hospital Infection, 84(2), 106–119.CrossRefGoogle Scholar
  7. Groopman, J. E. (2007). How Doctors Think. New York: Houghton Mifflin Company.Google Scholar
  8. Karimi, S., Wang, C., Metke-Jimenez, A., Gaire, R., & Paris, C. (2015b). Text and data mining techniques in adverse drug reaction detection. ACM Computing Surveys (CSUR), 47(4), 56.CrossRefGoogle Scholar
  9. Lee, D., Cornet, R., Lau, F., & De Keizer, N. (2013). A survey of SNOMED CT implementations. Journal of Biomedical Informatics, 46(1), 87–96.CrossRefGoogle Scholar
  10. Lee, D., de Keizer, N., Lau, F., & Cornet, R. (2014). Literature review of SNOMED CT use. Journal of the American Medical Informatics Association, 21(e1), e11–e19.CrossRefGoogle Scholar
  11. Meystre, S., Friedlin, J., South, B., Shen, S., & Samore, M. (2010). Automatic de-identification of textual documents in the electronic health record: A review of recent research. BMC Medical Research Methodology, 10(1), 70.CrossRefGoogle Scholar
  12. Meystre, S. M., Savova, G. K., Kipper-Schuler, K. C., & Hurdle, J. F. (2008). Extracting information from textual documents in the electronic health record: A review of recent research. Yearbook of Medical Informatics, 35, 128–144.Google Scholar
  13. Névéol, A., Dalianis, H., Savova, G., & Zweigenbaum, P. (2018). Clinical natural language processing in languages other than english: opportunities and challenges. Journal of Biomedical Semantics, 9(12), 1–13.Google Scholar
  14. Nguyen, A. N., Lawley, M. J., Hansen, D. P., Bowman, R. V., Clarke, B. E., Duhig, E. E., et al. (2010). Symbolic rule-based classification of lung cancer stages from free-text pathology reports. Journal of the American Medical Informatics Association, 17(4), 440–445.CrossRefGoogle Scholar
  15. Pratt, A. W., & Pacak, M. G. (1969). Automated processing of medical English. In Proceedings of the 1969 Conference on Computational Linguistics (pp. 1–23). Association for Computational Linguistics.Google Scholar
  16. Skeppstedt, M., Kvist, M., Nilsson, G., & Dalianis, H. (2014). Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: An annotation and machine learning study. In Journal of Biomedical Informatics, 49, 148–158.CrossRefGoogle Scholar
  17. Smith, K., Megyesi, B., Velupillai, S., & Kvist, M. (2014). Professional language in Swedish clinical text: Linguistic characterization and comparative studies. Nordic Journal of Linguistics, 37(02), 297–323.CrossRefGoogle Scholar
  18. Spasić, I., Livsey, J., Keane, J. A., & Nenadić, G. (2014). Text mining of cancer-related information: Review of current status and future directions. International Journal of Medical Informatics, 83(9), 605–623. Accessed 11 Jan 2018.CrossRefGoogle Scholar
  19. Velupillai, S., Dalianis, H., Hassel, M., & Nilsson, G. H. (2009). Developing a standard for de-identifying electronic patient records written in Swedish: Precision, recall and F-measure in a manual and computerized annotation trial. International Journal of Medical Informatics, 78(12), e19–e26.CrossRefGoogle Scholar
  20. Velupillai, S., Skeppstedt, M., Kvist, M., Mowery, D., Chapman, B. E., Dalianis, H., et al. (2014). Cue-based assertion classification for Swedish clinical text–Developing a lexicon for pyConTextSwe. Artificial Intelligence in Medicine, 61(3), 137–144.CrossRefGoogle Scholar

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Authors and Affiliations

  • Hercules Dalianis
    • 1
  1. 1.DSV-Stockholm UniversityKistaSweden

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