À la recherche du temps perdu: Extracting temporal relations from medical text in the 2012 i2b2 NLP challenge

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DOIResolve DOI: http://doi.org/10.1136/amiajnl-2013-001624
AuthorSearch for: ; Search for: ; Search for: ; Search for:
TypeArticle
Journal titleJournal of the American Medical Informatics Association
ISSN1067-5027
Volume20
Issue5
Pages843848; # of pages: 6
Subjectaccuracy; article; data extraction; hospital discharge; human; machine learning; medical record; medical specialist; prediction; semantics; temporal analysis; time
AbstractObjective: An analysis of the timing of events is critical for a deeper understanding of the course of events within a patient record. The 2012 i2b2 NLP challenge focused on the extraction of temporal relationships between concepts within textual hospital discharge summaries. Materials and methods: The team from the National Research Council Canada (NRC) submitted three system runs to the second track of the challenge: typifying the time-relationship between pre-annotated entities. The NRC system was designed around four specialist modules containing statistical machine learning classifiers. Each specialist targeted distinct sets of relationships: local relationships, 'sectime'-type relationships, non-local overlap-type relationships, and non-local causal relationships. Results: The best NRC submission achieved a precision of 0.7499, a recall of 0.6431, and an F1 score of 0.6924, resulting in a statistical tie for first place. Post hoc improvements led to a precision of 0.7537, a recall of 0.6455, and an F1 score of 0.6954, giving the highest scores reported on this task to date. Discussion and conclusions: Methods for general relation extraction extended well to temporal relations, and gave top-ranked state-of-the-art results. Careful ordering of predictions within result sets proved critical to this success.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); Information and Communication Technologies (ICT-TIC)
Peer reviewedYes
NPARC number21269694
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Record identifier66d4cef1-3ef2-475a-8142-f8f91780771a
Record created2013-12-13
Record modified2016-05-09
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