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Creating and Evaluating a Consensus for Negated and Speculative Words in a Swedish Clinical Corpus
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2010 (English)In: Proceedings of the Workshop on Negation and Speculation in Natural Language Processing ((NeSp-NLP 2010)) / [ed] Roser Morante, Caroline Sporleder, Antwerp: University of Antwerp , 2010, 5-13 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we describe the creation of a consensus corpus that was obtained through combining three individual annotations of the same clinical corpus in Swedish. We used a few basic rules that were executed automatically to create the consensus. The corpus contains negation words, speculative words, uncertain expressions and certain expressions. We evaluated the consensus using it for negation and speculation cue detection. We used Stanford NER, which is based on the machine learning algorithm Conditional Random Fields for the training and detection. For comparison we also used the clinical part of the BioScope Corpus and trained it with Stanford NER. For our clinical consensus corpus in Swedish we obtained a precision of 87.9 percent and a recall of 91.7 percent for negation cues, and for English with the Bioscope Corpus we obtained a precision of 97.6 percent and a recall of 96.7 percent for negation cues.

Place, publisher, year, edition, pages
Antwerp: University of Antwerp , 2010. 5-13 p.
National Category
Information Science
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-51878ISBN: 9789057282669 (print)OAI: oai:DiVA.org:su-51878DiVA: diva2:386345
Conference
Negation and Speculation in Natural Language Processing, NeSp-NLP 2010 NeSp-NLP 2010 Workshop, Uppsala, Sweden
Available from: 2011-01-12 Created: 2011-01-12 Last updated: 2013-11-29Bibliographically approved
In thesis
1. From Disorder to Order: Extracting clinical findings from unstructured text
Open this publication in new window or tab >>From Disorder to Order: Extracting clinical findings from unstructured text
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Medical disorders and findings are examples of important information in health record text. Through developing methods for automatically extracting these entities from the health record text, the possibility of making use of the information by automatic computerised processes increases. That a disorder or finding is mentioned in the health record, however, does not necessarily imply that it has been observed in the patient, because disorders that are ruled out and findings that are not observed in the patient are also mentioned.

This licentiate thesis investigates the possibility of automatically extracting disorders and findings from Swedish health record text and the possibility of automatically determining whether these findings and disorders are negated or not.

A rule- and terminology-based system that uses several Swedish medical terminologies, including SNOMED~CT and ICD-10 for extracting disorders, findings and body structures mentioned in Swedish clinical text was constructed and evaluated. Moreover, an English rule-based system for negation detection, NegEx, was adapted to Swedish and evaluated on clinical text written in Swedish.

The evaluation showed that disorders and findings were recognised with low recall, whereas body structures were recognised with comparatively good results. The negation detection system that was adapted to Swedish achieved the same recall as the English system, but lower precision.

The evaluated systems are accurate enough to be useful in some applications, but need to be further developed, especially when it comes to recognising disorders and findings.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2012. 79 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 12-005
Keyword
Text mining, named entity recognition, clinical language processing
National Category
Language Technology (Computational Linguistics)
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-95967 (URN)
Opponent
Supervisors
Available from: 2013-11-29 Created: 2013-11-07 Last updated: 2013-11-29Bibliographically approved

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