Abstract
This chapter specifically details the linguistic characteristics of patient record text in the form of spelling errors, domain specific abbreviations, negation and assertion expressions, etc. for English, Swedish and other languages.
Download chapter PDF
References
Afzal, Z., Pons, E., Kang, N., Sturkenboom, M. C. J. M., Schuemie, M. J., & Kors, J. A. (2014). ContextD: An algorithm to identify contextual properties of medical terms in a Dutch clinical corpus. BMC Bioinformatics, 15(1), 373.
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.
Aramaki, E., Miura, Y., Tonoike, M., Ohkuma, T., Masuichi, H., Waki, K., et al. (2010). Extraction of adverse drug effects from clinical records. Studies in Health Technology and Informatics, 160(Pt 1), 739–743.
Asamura, H., Wittekind, C., & Sobin, L. H. (2014). TNM Atlas: Illustrated Guide to the TNM Classification of Malignant Tumours. New York: Wiley.
Attardi, G., Cozza, V., & Sartiano, D. (2015). Annotation and extraction of relations from Italian medical records. In Proceedings of the 6th Italian Information Retrieval Workshop, Cagliari, Italy.
Boytcheva, S., Angelova, G., Angelov, Z., & Tcharaktchiev, D. (2015). Text mining and big data analytics for retrospective analysis of clinical texts from outpatient care. Cybernetics and Information Technologies, 15(4), 58–77.
Boytcheva, S., Nikolova, I., Angelova, G., & Angelov, Z. (2017b). Identification of risk factors in clinical texts through association rules. In Proceedings of RANLP Workshop on Biomedical Natural Language Processing (pp. 64–72).
Cederblom, S. (2005). Medicinska förkortningar och akronymer. Studentlitteratur, Lund.
Chapman, W. W., Bridewell, W., Hanbury, P., Cooper, G. F., & Buchanan, B. G. (2001). A simple algorithm for identifying negated findings and diseases in discharge summaries. Journal of Biomedical Informatics, 34(5), 301–310.
Chazard, E., Ficheur, G., Bernonville, S., Luyckx, M., & Beuscart, R. (2011). Data mining to generate adverse drug events detection rules. IEEE Transactions on Information Technology in Biomedicine, 15(6), 823–830.
Cotik, V., Filippo, D., Uszkoreit, H., & Xu, F. (2017). Annotation of entities and relations in Spanish radiology reports. In Proceedings of Recent Advances in Natural Language Processing, RANLP 2017, Varna, Bulgaria (pp. 177–184).
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).
Dalianis, H., Henriksson, A., Kvist, M., Velupillai, S., & Weegar, R. (2015). HEALTH BANK–A workbench for data science applications in healthcare. In J. Krogstie, G. Juel-Skielse, & V. Kabilan (Eds.), Proceedings of the CAiSE-2015 Industry Track Co-located with 27th Conference on Advanced Information Systems Engineering (CAiSE 2015), Stockholm, Sweden, June 11, 2015, CEUR (Vol. 1381, pp. 1–18). https://doi.org/urn:nbn:de:0074-1381-0E.
Dalianis, H., & Skeppstedt, M. (2010). Creating and evaluating a consensus for negated and speculative words in a Swedish clinical corpus. In Proceedings of the Workshop on Negation and Speculation in Natural Language Processing (pp. 5–13). Association for Computational Linguistics.
Ehrentraut, C., Tanushi, H., Tiedemann, J., & Dalianis, H. (2012). Detection of hospital acquired infections in sparse and noisy Swedish patient records. In Proceedings of the Sixth Workshop on Analytics for Noisy Unstructured Text Data (AND 2012) Held in Conjunction with Coling 2012, Bombay. ACM Digital Library.
Eriksson, R., Jensen, P. B., Frankild, S., Jensen, L. J., & Brunak, S. (2013). Dictionary construction and identification of possible adverse drug events in Danish clinical narrative text. Journal of the American Medical Informatics Association, 20(5), 947–953.
Grigonyte, G., Kvist, M., Velupillai, S., & Wirén, M. Improving readability of Swedish electronic health records through lexical simplification: First results. In Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations – PITR, Gothenburg, Sweden, April 2014 (pp. 74–83). Association for Computational Linguistics. http://www.aclweb.org/anthology/W14-1209. Accessed 11 Jan 2018.
Groopman, J. E. (2007). How Doctors Think. New York: Houghton Mifflin Company.
Grouin, C., & Névéol, A. (2014). De-identification of clinical notes in French: Towards a protocol for reference corpus development. Journal of Biomedical Informatics, 50, 151–161.
Isenius, N. (2012). Abbreviation Detection in Swedish Medical Records. The Development of SCAN, A Swedish Clinical Abbreviation Normalizer. Master’s thesis, Department of Computer and Systems Sciences, Stockholm University.
Isenius, N., Velupillai, S., & Kvist, M. (2012). Initial results in the development of SCAN. A Swedish clinical abbreviation normalizer. In CLEFeHealth 2012 Workshop on Cross-Language Evaluation of Methods, Applications, and Resources for eHealth Document Analysis, Rome.
Jensen, K., Soguero-Ruiz, C., Mikalsen, K. O., Lindsetmo, R.-O., Kouskoumvekaki, I., Girolami, M., et al. (2017). Analysis of free text in electronic health records for identification of cancer patient trajectories. Scientific Reports, 7, 46226.
Koeling, R., Carroll, J., Tate, A. R., & Nicholson, A. (2011). Annotating a corpus of clinical text records for learning to recognize symptoms automatically. In Proceedings of the 3rd Louhi Workshop on Text and Data Mining of Health Documents (pp. 43–50).
Kvist, M., & Velupillai, S. (2014). SCAN: A Swedish clinical abbreviation normalizer. Further development and adaptation to radiology. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 62–73). Berlin: Springer.
Lewis, J. D., Schinnar, R., Bilker, W. B., Wang, X., & Strom, B. L. (2007). Validation studies of the health improvement network (THIN) database for pharmacoepidemiology research. Pharmacoepidemiology and Drug safety, 16(4), 393–401.
Liu, H., Lussier, Y. A., & Friedman, C. (2001). A study of abbreviations in the UMLS. In AMIA Annual Symposium Proceedings (p. 393). American Medical Informatics Association.
Lövestam, E., Velupillai, S., & Kvist, M. (2014). Abbreviations in Swedish clinical text - Use by three professions. Studies in Health Technology and Informatics, 205, 720–724. https://doi.org/10.3233/978-1-61499-432-9-720.
Marciniak, M., & Mykowiecka, A. (2014). Terminology extraction from medical texts in Polish. Journal of Biomedical Semantics, 5(1), 24.
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., Moore, J., O’Dwyer, J., & Philpot, S. (2016). Automated cancer registry notifications: validation of a medical text analytics system for identifying patients with cancer from a state-wide pathology repository. In AMIA Annual Symposium Proceedings (pp. 964–973). American Medical Informatics Association.
Nizamuddin, N., & Dalianis, H. (2014). Detection of spelling errors in Swedish clinical text. In 1st Nordic Workshop on Evaluation of Spellchecking and Proofing Tools (NorWEST2014), SLTC 2014.
Olsson, M. (2011). Vem begriper patientjournalen? (In Swedish). Bachelor’s thesis, Linnaeus University.
Pakhomov, S., Pedersen, T., & Chute, C. G. (2005). Abbreviation and acronym disambiguation in clinical discourse. In AMIA Annual Symposium Proceedings (Vol. 2005, p. 589). American Medical Informatics Association.
Pantazos, K., Lauesen, S., & Lippert, S. (2016). Preserving medical correctness, readability and consistency in de-identified health records. Health Informatics Journal, 23(4), 291–303.
Patrick, J., & Nguyen, D. (2011). Automated proof reading of clinical notes. In PACLIC, 25th Pacific Asia Conference on Language, Information and Computation (pp. 303–312).
Perera, G., Broadbent, M., Callard, F., Chang, C.-K., Downs, J., Dutta, R., et al. (2016). Cohort profile of the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register: current status and recent enhancement of an electronic mental health record-derived data resource. BMJ Open, 6(3), e008721.
Pérez, A., Weegar, R., Casillas, A., Gojenola, K., Oronoz, M., & Dalianis, H. (2017). Semi-supervised medical entity recognition: A study on Spanish and Swedish clinical corpora. Journal of Biomedical Informatics, 71, 16–30.
Pestian, J. P., Brew, C., Matykiewicz, P., Hovermale, D. J., Johnson, N., Cohen, K. B., et al. (2007). A shared task involving multi-label classification of clinical free text. In Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing (pp. 97–104). Association for Computational Linguistics.
Proux, D., Hagège, C., Gicquel, Q., Pereira, S., Darmoni, S., Segond, F., et al. (2011). Architecture and systems for monitoring hospital acquired infections inside a hospital information workflow. In Proceedings of the Workshop on Biomedical Natural Language Processing. USA: Portland, Oregon (p. 43e48). Citeseer.
Roberts, A., Gaizauskas, R., Hepple, M., Demetriou, G., Guo, Y., Roberts, I., et al. (2009). Building a semantically annotated corpus of clinical texts. Journal of Biomedical Informatics, 42(5), 950–966.
Roller, R., Uszkoreit, H., Xu, F., Seiffe, L., Mikhailov, M., Staeck, O., et al. (2016). A fine-grained corpus annotation schema of German nephrology records. In Proceedings of the Clinical Natural Language Processing Workshop, Osaka, Japan, December 11–17 (pp. 69–77).
Ruch, P., Robert, B., & Antoine, G. (2003). Using lexical disambiguation and named-entity recognition to improve spelling correction in the electronic patient record. Artificial Intelligence in Medicine, 29(1), 169–184.
Saeed, M., Villarroel, M., Reisner, A. T., Clifford, G., Lehman, L.-W., Moody, G., et al. (2011). Multiparameter intelligent monitoring in intensive care II (MIMIC-II): A public-access intensive care unit database. Critical Care Medicine, 39(5), 952.
Saurí, R., & Pustejovsky, J. (2009). Factbank: A corpus annotated with event factuality. Language Resources and Evaluation, 43(3), 227–268.
Siklósi, B., Novák, A., & Prószéky, G. (2014). Resolving abbreviations in clinical texts without pre-existing structured resources. In Fourth Workshop on Building and Evaluating Resources for Health and Biomedical Text Processing, LREC (Vol. 2014).
Skeppstedt, M., Kvist, M., & Dalianis, H. (2012). Rule-based entity recognition and coverage of SNOMED CT in Swedish clinical text. In Proceedings of the Eighth International Conference on Language Resources and Evaluation, LREC 2012 (pp. 1250–1257).
Spat, S., Cadonna, B., Rakovac, I., Gütl, C., Leitner, H., Stark, G., et al. (2008). Enhanced information retrieval from narrative German-language clinical text documents using automated document classification. Studies in Health Technology and Informatics, 136, 473.
Velupillai, S. (2011). Automatic classification of factuality levels: A case study on Swedish diagnoses and the impact of local context. In Fourth International Symposium on Languages in Biology and Medicine, LBM 2011.
Velupillai, S. (2012). Shades of Certainty: Annotation and Classification of Swedish Medical Records. PhD thesis, Stockholm University.
Velupillai, S., Dalianis, H., & Kvist, M. (2011). Factuality levels of diagnoses in Swedish clinical text. In MIE-Medical Informatics Europe (pp. 559–563). http://dx.doi.org/10.3233/978-1-60750-806-9-559.
Vincze, V., Szarvas, G., Farkas, R., Móra, G., & Csirik, J. (2008). The BioScope Corpus: Biomedical texts annotated for uncertainty, negation and their scopes. BMC Bioinformatics, 9(Suppl 11), S9.
Weegar, R., & Dalianis, H. (2015). Creating a rule based system for text mining of Norwegian breast cancer pathology reports. In Sixth International Workshop in Health Text Mining and Information Analysis (LOUHI), Held in Conjunction with EMNLP 2015, Lisbon, Portugal (pp. 73–78).
Wu, Y., Rosenbloom, S. T., Denny, J. C., Miller, R. A., Mani, S., Giuse, D. A., et al. (2011). Detecting abbreviations in discharge summaries using machine learning methods. In AMIA Annual Symposium Proceedings (Vol. 2011, p. 1541). American Medical Informatics Association.
Zhang, S., Kang, T., Zhang, X., Wen, D., Elhadad, N., & Lei, J. (2016). Speculation detection for Chinese clinical notes: Impacts of word segmentation and embedding models. Journal of Biomedical Informatics, 60, 334–341.
Zubke, M. (2017). Classification based extraction of numeric values from clinical narratives. In Proceedings of RANLP Workshop on Biomedical Natural Language Processing (pp. 24–31).
Author information
Authors and Affiliations
Rights and permissions
This chapter is published under an open access license. Please check the 'Copyright Information' section either on this page or in the PDF for details of this license and what re-use is permitted. If your intended use exceeds what is permitted by the license or if you are unable to locate the licence and re-use information, please contact the Rights and Permissions team.
Copyright information
© 2018 The Author(s)
About this chapter
Cite this chapter
Dalianis, H. (2018). Characteristics of Patient Records and Clinical Corpora. In: Clinical Text Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-78503-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-78503-5_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78502-8
Online ISBN: 978-3-319-78503-5
eBook Packages: Computer ScienceComputer Science (R0)