{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T18:06:51Z","timestamp":1690049211952},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Objective To improve the normalization of relative and incomplete temporal expressions (RI-TIMEXes) in clinical narratives.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>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.<\/jats:p>","DOI":"10.1093\/jamia\/ocu004","type":"journal-article","created":{"date-parts":[[2015,4,14]],"date-time":"2015-04-14T00:11:41Z","timestamp":1428970301000},"page":"1001-1008","source":"Crossref","is-referenced-by-count":9,"title":["Normalization of relative and incomplete temporal expressions in clinical narratives"],"prefix":"10.1093","volume":"22","author":[{"given":"Weiyi","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Informatics, University at Albany, SUNY. Albany, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna","family":"Rumshisky","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Massachusetts Lowell. 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