Hostname: page-component-5d84bcc8dc-p7rnh Total loading time: 0 Render date: 2026-09-10T17:37:09.885Z Has data issue: false hasContentIssue false
  • English
  • Français

Professional language in Swedish clinical text: Linguistic characterization and comparative studies

Published online by Cambridge University Press:  16 October 2014

Kelly Smith ,
Beata Megyesi ,
Sumithra Velupillai  and
Maria Kvist
Kelly Smith
Affiliation:
Department of Computer and Systems Sciences, Stockholm University, Postbox 7003, 164 07 Kista, Sweden. kellys@dsv.su.se
Beata Megyesi
Affiliation:
Department of Linguistics and Philology, Uppsala University, Postbox 635, S-751 26 Uppsala, Sweden. beata.megyesi@lingfil.uu.se
Sumithra Velupillai
Affiliation:
Department of Computer and Systems Sciences, Stockholm University, Postbox 7003, 164 07 Kista, Sweden. sumithra@dsv.su.se
Maria Kvist
Affiliation:
Department of Computer and Systems Sciences, Stockholm University, Postbox 7003, 164 07 Kista, Sweden & Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet, Sweden. maria.kvist@karolinska.se
Get access

Abstract

This study investigates the linguistic characteristics of Swedish clinical text in radiology reports and doctor's daily notes from electronic health records (EHRs) in comparison to general Swedish and biomedical journal text. We quantify linguistic features through a comparative register analysis to determine how the free text of EHRs differ from general and biomedical Swedish text in terms of lexical complexity, word and sentence composition, and common sentence structures. The linguistic features are extracted using state-of-the-art computational tools: a tokenizer, a part-of-speech tagger, and scripts for statistical analysis. Results show that technical terms and abbreviations are more frequent in clinical text, and lexical variance is low. Moreover, clinical text frequently omit subjects, verbs, and function words resulting in shorter sentences. Clinical text not only differs from general Swedish, but also internally, across its sub-domains, e.g. sentences lacking verbs are significantly more frequent in radiology reports. These results provide a foundation for future development of automatic methods for EHR simplification or clarification.

Keywords

clinical textcomparative register analysisdoctor’s daily noteselectronic health recordsmedical terminologypart-of-speech taggingradiology reports

Information

Type
Research Article
Information
Nordic Journal of Linguistics , Volume 37 , Issue 2: Corpus Linguistics and the Nordic Languages , October 2014 , pp. 297 - 323
Copyright
Copyright © Nordic Association of Linguistics 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Aantaa, Kirsi. 2012. Mot patientvänligare epikriser. En kontrastiv undersökning [Towards more patient friendly discharge letters: A contrastive study]. MA thesis, Department of Nordic Languages, University of Turku.Google Scholar
Adnan, Mehnaz, Warren, Jim & Orr, Martin. 2010. Assessing text characteristics of electronic discharge summaries and their implications for patient readability. Proceedings of the Fourth Australasian Workshop on Health Informatics and Knowledge Management 108, 77–84.Google Scholar
Allvin, Helen. 2010. Patientjournalen som genre. En text- och genreanalys om patientjournalers relation till patientdatalagen [The patient record as genre: A text and genre analysis of the relationship of patient records and the Patient Data Act]. MA thesis, Department of Nordic Languages, Stockholm University.Google Scholar
Allvin, Helen, Carlsson, Elin, Dalianis, Hercules, Danielsson-Ojala, Riitta, Daudaravicius, Vidas, Hassel, Martin, Kokkinakis, Dimitrios, Lundgren-Laine, Heljö, Nilsson, Gunnar H, Nytrø, Øystein, Salanterä, Sanna, Skeppstedt, Maria, Suominen, Hanna & Velupillai, Sumithra. 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):S1.Google Scholar
Aramaki, Eiji, Miura, Yasuhide, Tonoike, Masatsugu, Ohkuma, Tomoko, Mashuichi, Hiroshi & Ohe, Kazuhiko. 2009. TEXT2TABLE: Medical Text summarization system based on named entity recognition and modality identification. Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing (BioNLP ’09), 185–192.Google Scholar
Biber, Douglas & Conrad, Susan. 2009. Register, Genre, and Style. Cambridge: Cambridge University Press.Google Scholar
Borin, Lars, Grabar, Natalia, Gronostaj, Maria Toporowska, Hallett, Catalina, Hardcastle, David, Kokkinakis, Dimitrios, Williams, Sandra & Willis, Alistair. 2009. Semantic Mining Deliverable D27.2: Empowering the Patient with Language Technology (Technical Report Semantic Mining, NOE 507505), 1–75. Göteborg: Göteborg University.Google Scholar
Bretschneider, Claudia, Zillner, Sonja & Hammon, Matthias. 2013. Identifying pathological findings in German radiology reports using a syntacto-semantic parsing approach. Proceedings of the 2013 Workshop on Biomedical Natural Language Processing (BioNLP ’13), 27–35.Google Scholar
Campbell, David A. & Johnson, Stephen B.. 2001. Comparing syntactic complexity in medical and non-medical corpora. AMIA Annual Symposium Proceedings 2001, 90–94.Google Scholar
Coden, Anni R., Pakhomov, Serguei V., Ando, Rie K., Duffy, Patrick H. & Chute, Christopher G.. 2005. Domain-specific language models and lexicons for tagging. Journal of Biomedical Informatics 38 (6), 422–430.Google Scholar
Dalianis, Hercules, Hassel, Martin, Henriksson, Aron & Skeppstedt, Maria. 2012. Stockholm EPR Corpus: A clinical database used to improve health care. Proceedings of Fourth Swedish Language Technology Conference, 17–18.Google Scholar
Dalianis, Hercules, Hassel, Martin & Velupillai, Sumithra. 2009. The Stockholm EPR Corpus – characteristics and some initial findings. Proceedings of the 14th International Symposium on Health Information Management Research – ISHIMR 2009, 243–249.Google Scholar
Fan, Jung Wei, Yang, Elly W., Jiang, Min, Prasad, Rashmi, Loomis, Richard M., Zisook, Daniel S., Denny, Josh C., Xu, Hua & Huang, Yang. 2013. Syntactic parsing of clinical text: Guideline and corpus development with handling ill-formed sentences. Journal of the American Medical Informatics Association 20, 1–10.Google Scholar
Ferraro, Jeffrey P., Daumé III, Hal, DuVall, Scott L., Chapman, Wendy Webber, Harkema, Henk & Haug, Peter J.. 2013. Improving performance of natural language processing part-of-speech tagging on clinical narratives through domain adaptation. Journal of the American Medical Informatics Association 20 (5), 931–939.Google Scholar
Friedman, Carol, Kra, Pauline & Rzhetsky, Andrey. 2002. Two biomedical sublanguages: A description based on the theories of Zellig Harris. Journal of Biomedical Informatics 35 (4), 222–235.Google Scholar
Grigonyté, Gintaré, Kvist, Maria, Velupillai, Sumithra & Wirén, Mats. 2014. Improving readability of Swedish electronic health records through lexical simplification: First results. Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR)@EACL, 74–83.Google Scholar
Hahn, Udo & Wermter, Joachim. 2004. High-performance tagging on medical texts. Proceedings of the 20th International Conference on Computational Linguistics (COLING ’04), 973–979.Google Scholar
Isenius, Niklas, Velupillai, Sumithra & Kvist, Maria. 2012. Initial results in the development of SCAN: A Swedish clinical abbreviation normalizer. Proceedings of the CLEF 2012 Workshop on Cross-language Evaluation of Methods, Applications, and Resources for eHealth Document Analysis.Google Scholar
Keselman, Alla, Slaughter, Laura, Smith, Catherine Arnott, Kim, Hyeoneui, Divita, Guy, Browne, Allen, Tsai, Christopher & Zeng-Treitler, Qing. 2007. Towards consumer-friendly PHRs: Patients’ experience with reviewing their health records. Proceedings of the Eighth International Conference on Language Resources and Evaluation, 399–403.Google Scholar
Kokkinakis, Dimitrios. 2012. The journal of the Swedish Medical Association – a corpus resource for biomedical text mining in Swedish. Proceedings of the 3rd Workshop on Building and Evaluating for Biomedical Text Mining (BioTxtM), LREC 2012 Workshop, 40–44.Google Scholar
Krauthammer, Michael & Nenadic, Goran. 2004. Term identification in the biomedical literature. Journal of Biomedical Informatics 37 (6), 512–526.Google Scholar
Kvist, Maria, Skeppstedt, Maria, Velupillai, Sumithra & Dalianis, Hercules. 2011. Modeling human comprehension of Swedish medical records for intelligent access and summarization systems – future vision, a physician's perspective. Proceedings of Scandinavian Health Informatics Meeting, 31–35.Google Scholar
Kvist, Maria & Velupillai, Sumithra. 2013. Professional language in Swedish radiology reports – characterization for patient-adapted text simplification. Proceedings of Scandinavian Conference on Health Informatics, 55–60.Google Scholar
Liu, Hongfang, Lussier, Yves A. & Friedman, Carol. 2001. A study of abbreviations in the UMLS. AMIA Annual Symposium Proceedings 2001, 393–397.Google Scholar
Melin, Lars. 2004. Fattaru?! [Do ya get it?!]. Forskning och Framsteg 3.Google Scholar
Meystre, Stephane M., Savova, Guergana K., Kipper-Schuler, Karin C. & Hurdle, John F. 2008. Extracting information from textual documents in the electronic health record: A review of recent research. IMIA Yearbook of Medical Informatics 47 (S1), 128–144.Google Scholar
Mühlenbock, Katarina & Kokkinakis, Sofie Johansson. 2009. LIX 68 revisited – an extended readability measure. Proceedings of Corpus Linguistics 2009, http://ucrel.lancs.ac.uk/publications/cl2009/364_FullPaper.doc.Google Scholar
Olsson, May. 2011. Vem begriper patientjournalen? [Who comprehends the patient record?]. BA thesis, Department of Language and Literature, Linné University.Google Scholar
Östling, Robert. 2013. Stagger: An open-source part of speech tagger for Swedish. Northern European Journal of Language Technology 3, 1–18.Google Scholar
Ownby, Raymond. 2005. Influence of vocabulary and sentence complexity and passive voice on the readability of consumer-oriented mental health information on the Internet. AMIA Annual Symposium Proceedings 2005, 585–588.Google Scholar
Pakhomov, Serguei, Pedersen, Ted & Chute, Christopher G.. 2005. Abbreviation and acronym disambiguation in clinical discourse. AMIA Annual Symposium Proceedings 2005, 589–593.Google Scholar
Patrick, Jon, Sabbagh, Mojtaba, Jain, Suvir & Zheng, Haifeng. 2010. Spelling correction in clinical notes with emphasis on first suggestion accuracy. Proceedings of the 2nd Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM), 2–8.Google Scholar
Pyper, Cecilia, Amery, Justin, Watson, Marion & Crook, Claire. 2004. Patients’ experiences when accessing their on-line electronic patient records in primary care. The British Journal of General Practice 54, 38–43.Google Scholar
Skeppstedt, Maria, Kvist, Maria & Dalianis, Hercules. 2012. Rule-based entity recognition and coverage of SNOMED CT in Swedish clinical text. Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12), 1250–1257.Google Scholar
Smedby, Björn. 1991. Medicinens Språk: språket i sjukdomsklassifikationen – mer konsekvent försvenskning eftersträvas [Language of medicine: The language of diagnose classification – more consistent Swedification sought]. Läkartidningen 88, 1519–1520.Google Scholar
Smith, Christian, Danielsson, Henrik & Jönsson, Arne. 2012. A good space: Lexical predictors in word space evaluation. Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12), 2530–2535.Google Scholar
Tomanek, Katrin, Wermter, Joachim & Hahn, Udo. 2007. A reappraisal of sentence and token splitting for life sciences documents. Proceedings of 12th World Congress on Health (Medical) Informatics – Building Sustainable Health Systems, 524–528.Google Scholar
Xu, Hua, Stetson, Peter & Friedman, Carol. 2007. A study of abbreviations in clinical notes. AMIA Annual Symposium Proceedings 2007, 821–825.Google Scholar