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Retrieving disorders and findings: Results using SNOMED CT and NegEx adapted for Swedish
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
ent of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm.
2011 (English)In: LOUHI 2011 Health Document Text Mining and Information Analysis 2011: Proceedings of LOUHI 2011 Third International Workshop on Health Document Text Mining and Information AnalysisBled, Slovenia, July 6, 2011. / [ed] Øystein Nytrø, Laura Slaughter, Hans Moen, 2011, 11-17 p.Conference paper, Published paper (Other academic)
Abstract [en]

Access to reliable data from electronic health records is of high importance in several key areas in patient care, biomedical research, and education. However, many of the clinical entities are negated in the patient record text. Detecting what is a negation and what is not is therefore a key to high quality text mining. In this study we used the NegEx system adapted for Swedish to investigate negated clinical entities. We applied the system to a subset of free-text entries under a heading containing the word ‘assessment’ from the Stockholm EPR corpus, containing in total 23,171,559 tokens. Specifically, the explored entities were the SNOMED CT terms having the semantic categories ‘finding’ or ‘disorder’. The study showed that the proportion of negated clinical entities was around 9%. The results thus support that negations are abundant in clinical text and hence negation detection is vital for high quality text mining in the medical domain.

Place, publisher, year, edition, pages
2011. 11-17 p.
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 744
Keyword [en]
Negation detection, Clinical text, Electronic patient records, SNOMED CT, Swedish
Keyword [sv]
Negationsdetektion, Klinisk text, Elektroniska patientjournaler, SNOMED CT, Svenska
National Category
Information Science
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-62354OAI: oai:DiVA.org:su-62354DiVA: diva2:441279
Conference
Third International Workshop on Health Document Text Mining and Information AnalysisBled, Slovenia, July 6, 2011, Bled Slovenia, Collocated with AIME 2011.
Available from: 2011-09-15 Created: 2011-09-15 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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CiteExportLink to record
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Citation style
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  • de-DE
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  • Other locale
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Output format
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