Eventual situations for timeline extraction from clinical reports.
Objective: To identify the temporal relations between clinical events and temporal expressions in clinical reports, as defined in the i2b2/VA 2012 challenge.Design: To detect clinical events, we used rules and Conditional Random Fields. We built Random Forest models to identify event modality and po...
| Published in: | Journal of the American Medical Informatics Association Vol. 20; no. 5; pp. 820 - 828 |
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| Main Authors: | , , , , , |
| Format: | research Journal Article |
| Published: |
Oxford University Press / USA
Sep2013
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104086793&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104086793 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Sep2013 vid: 20 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104086793 NLM23571851 2012222029 10.1136/amiajnl-2013-001627 NLM23571851 PMC3756272 104086793 ppf: 820 ppct: 8 formats: tig: atl: Eventual situations for timeline extraction from clinical reports. aug: au: Grouin, Cyril Grabar, Natalia Hamon, Thierry Rosset, Sophie Tannier, Xavier Zweigenbaum, Pierre affil: LIMSI-CNRS, Orsay, France. sug: subj: Electronic Health Records Information Retrieval Methods Natural Language Processing Artificial Intelligence Human Time ab: Objective: To identify the temporal relations between clinical events and temporal expressions in clinical reports, as defined in the i2b2/VA 2012 challenge.Design: To detect clinical events, we used rules and Conditional Random Fields. We built Random Forest models to identify event modality and polarity. To identify temporal expressions we built on the HeidelTime system. To detect temporal relations, we systematically studied their breakdown into distinct situations; we designed an oracle method to determine the most prominent situations and the most suitable associated classifiers, and combined their results.Results: We achieved F-measures of 0.8307 for event identification, based on rules, and 0.8385 for temporal expression identification. In the temporal relation task, we identified nine main situations in three groups, experimentally confirming shared intuitions: within-sentence relations, section-related time, and across-sentence relations. Logistic regression and Naïve Bayes performed best on the first and third groups, and decision trees on the second. We reached a 0.6231 global F-measure, improving by 7.5 points our official submission.Conclusions: Carefully hand-crafted rules obtained good results for the detection of events and temporal expressions, while a combination of classifiers improved temporal link prediction. The characterization of the oracle recall of situations allowed us to point at directions where further work would be most useful for temporal relation detection: within-sentence relations and linking History of Present Illness events to the admission date. We suggest that the systematic situation breakdown proposed in this paper could also help improve other systems addressing this task. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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