Abstract
This chapter gives a short introduction of the research area of clinical text mining.
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References
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.
Cohen, K. B., & Demner-Fushman, D. (2014). Biomedical Natural Language Processing (Vol. 11). Amsterdam: John Benjamins Publishing Company.
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.
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).
Ducel, G., Fabry, J., & Nicolle, L. (Eds.). (2002). Prevention of Hospital Acquired Infections: A Practical Guide., 2nd edn. World Health Organization. http://www.who.int/csr/resources/publications/drugresist/WHO_CDS_CSR_EPH_2002_12/en/. Accessed 11 Jan 2018.
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.
Groopman, J. E. (2007). How Doctors Think. New York: Houghton Mifflin Company.
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.
Lee, D., Cornet, R., Lau, F., & De Keizer, N. (2013). A survey of SNOMED CT implementations. Journal of Biomedical Informatics, 46(1), 87–96.
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.
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.
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.
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.
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.
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.
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.
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.
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. http://dx.doi.org/10.1016/j.ijmedinf.2014.06.009. Accessed 11 Jan 2018.
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.
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.
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Dalianis, H. (2018). Introduction. In: Clinical Text Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-78503-5_1
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DOI: https://doi.org/10.1007/978-3-319-78503-5_1
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