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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

Department of Informatics, University at Albany, SUNY. Albany, NY
Department of Computer Science, University of Massachusetts Lowell. Lowell, MA.
Department of Information Studies, University at Albany, SUNY. Albany, NY.



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


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.


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.


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.


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.


medical language processing; temporal expression normalization; temporal reasoning

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