Rule-based Entity Recognition and Coverage of SNOMED CT in Swedish Clinical Text
2012 (English)In: LREC 2012 8th ELRA Conference on Language Resources and Evaluation: Proceedings, European Language Resources Association (ELRA) , 2012, 1250-1257 p.Conference paper (Refereed)
Named entity recognition of the clinical entities disorders, ﬁndings and body structures is needed for information extraction from unstructured text in health records. Clinical notes from a Swedish emergency unit were annotated and used for evaluating a rule- and terminology-based entity recognition system. This system used different preprocessing techniques for matching terms to SNOMED CT, and, one by one, four other terminologies were added. For the class body structure, the results improved with preprocessing, whereas only small improvements were shown for the classes disorder and ﬁnding. The best average results were achieved when all terminologies were used together. The entity body structure was recognised with a precision of 0.74 and a recall of 0.80, whereas lower results were achieved for disorder (precision: 0.75, recall: 0.55) and for ﬁnding (precision: 0.57, recall: 0.30). The proportion of entities containing abbreviations were higher for false negatives than for correctly recognised entities, and no entities containing more than two tokens were recognised by the system. Low recall for disorders and ﬁndings shows both that additional methods are needed for entity recognition and that there are many expressions in clinical text that are not included in SNOMED CT.
Place, publisher, year, edition, pages
European Language Resources Association (ELRA) , 2012. 1250-1257 p.
Electronic patient records, Swedish, SNOMED CT, named entity recognition
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-82257ISI: 000323927701056ISBN: 978-2-9517408-7-7OAI: oai:DiVA.org:su-82257DiVA: diva2:567235
8th International Conference on Language Resources and Evaluation (LREC), Istanbul, Turkey, 23-25 May, 2012