ArticleJournal of the American Medical Informatics Association : JAMIA2015
Normalization of relative and incomplete temporal expressions in clinical narratives.
Article in Journal of the American Medical Informatics Association : JAMIA, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach.JMIR medical informatics · 2020Article
- Temporal information extraction from mental health records to identify duration of untreated psychosis.Journal of biomedical semantics · 2020Article
- Defining Phenotypes from Clinical Data to Drive Genomic Research.Annual review of biomedical data science · 2018Article
- A bibliometric analysis of natural language processing in medical research.BMC medical informatics and decision making · 2018Article
- Clinical Natural Language Processing in 2015: Leveraging the Variety of Texts of Clinical Interest.Yearbook of medical informatics · 2016Article
- An information model for computable cancer phenotypes.BMC medical informatics and decision making · 2016Article
- Optimizing annotation resources for natural language de-identification via a game theoretic framework.Journal of biomedical informatics · 2016Article
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Abstract
objectiveTo improve the normalization of relative and incomplete temporal expressions (RI-TIMEXes) in clinical narratives.
methodsWe 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.
resultsThe 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.
conclusionsWe 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.
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