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J Am Med Inform Assoc. 2015 Sep;22(5):1001-8. doi: 10.1093/jamia/ocu004. Epub 2015 Apr 12.

Normalization of relative and incomplete temporal expressions in clinical narratives.

Author information

1
Department of Informatics, University at Albany, SUNY. Albany, NY wsun2@albany.edu.
2
Department of Computer Science, University of Massachusetts Lowell. Lowell, MA.
3
Department of Information Studies, University at Albany, SUNY. Albany, NY.

Abstract

OBJECTIVE:

To improve the normalization of relative and incomplete temporal expressions (RI-TIMEXes) in clinical narratives.

METHODS:

We analyzed the RI-TIMEXes in temporally annotated corpora and propose two hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative domain: the anchor point hypothesis and the anchor relation hypothesis. We annotated the RI-TIMEXes in three corpora to study the characteristics of RI-TMEXes in different domains. This informed the design of our RI-TIMEX normalization system for the clinical domain, which consists of an anchor point classifier, an anchor relation classifier, and a rule-based RI-TIMEX text span parser. We experimented with different feature sets and performed an error analysis for each system component.

RESULTS:

The annotation confirmed the hypotheses that we can simplify the RI-TIMEXes normalization task using two multi-label classifiers. Our system achieves anchor point classification, anchor relation classification, and rule-based parsing accuracy of 74.68%, 87.71%, and 57.2% (82.09% under relaxed matching criteria), respectively, on the held-out test set of the 2012 i2b2 temporal relation challenge.

DISCUSSION:

Experiments with feature sets reveal some interesting findings, such as: the verbal tense feature does not inform the anchor relation classification in clinical narratives as much as the tokens near the RI-TIMEX. Error analysis showed that underrepresented anchor point and anchor relation classes are difficult to detect.

CONCLUSIONS:

We formulate the RI-TIMEX normalization problem as a pair of multi-label classification problems. Considering only RI-TIMEX extraction and normalization, the system achieves statistically significant improvement over the RI-TIMEX results of the best systems in the 2012 i2b2 challenge.

KEYWORDS:

medical language processing; temporal expression normalization; temporal reasoning

PMID:
25868462
PMCID:
PMC4986666
DOI:
10.1093/jamia/ocu004
[Indexed for MEDLINE]
Free PMC Article

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