Comprehensive temporal information detection from clinical text: medical events, time, and TLINK identification.
Background: Temporal information detection systems have been developed by the Mayo Clinic for the 2012 i2b2 Natural Language Processing Challenge.Objective: To construct automated systems for EVENT/TIMEX3 extraction and temporal link (TLINK) identification from clinical text.Materials and Methods: T...
| Published in: | Journal of the American Medical Informatics Association Vol. 20; no. 5; pp. 836 - 843 |
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| Main Authors: | , , , , , , |
| Format: | research Journal Article |
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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=104086790&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104086790 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: 104086790 NLM23558168 2012222025 10.1136/amiajnl-2013-001622 NLM23558168 PMC3756269 104086790 ppf: 836 ppct: 7 formats: tig: atl: Comprehensive temporal information detection from clinical text: medical events, time, and TLINK identification. aug: au: Sohn, Sunghwan Wagholikar, Kavishwar B Li, Dingcheng Jonnalagadda, Siddhartha R Tao, Cui Komandur Elayavilli, Ravikumar Liu, Hongfang affil: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA. sug: subj: Artificial Intelligence Electronic Health Records Information Retrieval Methods Natural Language Processing Patient Discharge Human Time ab: Background: Temporal information detection systems have been developed by the Mayo Clinic for the 2012 i2b2 Natural Language Processing Challenge.Objective: To construct automated systems for EVENT/TIMEX3 extraction and temporal link (TLINK) identification from clinical text.Materials and Methods: The i2b2 organizers provided 190 annotated discharge summaries as the training set and 120 discharge summaries as the test set. Our Event system used a conditional random field classifier with a variety of features including lexical information, natural language elements, and medical ontology. The TIMEX3 system employed a rule-based method using regular expression pattern match and systematic reasoning to determine normalized values. The TLINK system employed both rule-based reasoning and machine learning. All three systems were built in an Apache Unstructured Information Management Architecture framework.Results: Our TIMEX3 system performed the best (F-measure of 0.900, value accuracy 0.731) among the challenge teams. The Event system produced an F-measure of 0.870, and the TLINK system an F-measure of 0.537.Conclusions: Our TIMEX3 system demonstrated good capability of regular expression rules to extract and normalize time information. Event and TLINK machine learning systems required well-defined feature sets to perform well. We could also leverage expert knowledge as part of the machine learning features to further improve TLINK identification performance. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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