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J Am Med Inform Assoc. 2013 Sep-Oct;20(5):843-8. doi: 10.1136/amiajnl-2013-001624. Epub 2013 Mar 23.

A la Recherche du Temps Perdu: extracting temporal relations from medical text in the 2012 i2b2 NLP challenge.

Author information

1
Information and Communication Technologies, National Research Council Canada, Ottawa, Ontario, Canada.

Abstract

OBJECTIVE:

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.

KEYWORDS:

clinical text; information extraction; natural language processing; relation extraction; temporal reasoning

PMID:
23523875
PMCID:
PMC3756270
DOI:
10.1136/amiajnl-2013-001624
[Indexed for MEDLINE]
Free PMC Article
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