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J Am Med Inform Assoc. 2013 Sep-Oct;20(5):806-13. doi: 10.1136/amiajnl-2013-001628. Epub 2013 Apr 5.

Evaluating temporal relations in clinical text: 2012 i2b2 Challenge.

Author information

1
Department of Informatics, University at Albany, SUNY, Albany, New York, USA.

Abstract

BACKGROUND:

The Sixth Informatics for Integrating Biology and the Bedside (i2b2) Natural Language Processing Challenge for Clinical Records focused on the temporal relations in clinical narratives. The organizers provided the research community with a corpus of discharge summaries annotated with temporal information, to be used for the development and evaluation of temporal reasoning systems. 18 teams from around the world participated in the challenge. During the workshop, participating teams presented comprehensive reviews and analysis of their systems, and outlined future research directions suggested by the challenge contributions.

METHODS:

The challenge evaluated systems on the information extraction tasks that targeted: (1) clinically significant events, including both clinical concepts such as problems, tests, treatments, and clinical departments, and events relevant to the patient's clinical timeline, such as admissions, transfers between departments, etc; (2) temporal expressions, referring to the dates, times, durations, or frequencies phrases in the clinical text. The values of the extracted temporal expressions had to be normalized to an ISO specification standard; and (3) temporal relations, between the clinical events and temporal expressions. Participants determined pairs of events and temporal expressions that exhibited a temporal relation, and identified the temporal relation between them.

RESULTS:

For event detection, statistical machine learning (ML) methods consistently showed superior performance. While ML and rule based methods seemed to detect temporal expressions equally well, the best systems overwhelmingly adopted a rule based approach for value normalization. For temporal relation classification, the systems using hybrid approaches that combined ML and heuristics based methods produced the best results.

KEYWORDS:

clinical language processing; medical language processing; natural language processing; sharedtask challenges; temporal reasoning

PMID:
23564629
PMCID:
PMC3756273
DOI:
10.1136/amiajnl-2013-001628
[Indexed for MEDLINE]
Free PMC Article

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