An end-to-end system to identify temporal relation in discharge summaries: 2012 i2b2 challenge.
Objective: To create an end-to-end system to identify temporal relation in discharge summaries for the 2012 i2b2 challenge. The challenge includes event extraction, timex extraction, and temporal relation identification.Design: An end-to-end temporal relation system was developed. It includes three...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 20; no. 5; pp. 849 - 859 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Sep2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104086788&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104086788 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: 104086788 NLM23467472 2012222021 10.1136/amiajnl-2012-001607 NLM23467472 PMC3756267 104086788 ppf: 849 ppct: 10 formats: tig: atl: An end-to-end system to identify temporal relation in discharge summaries: 2012 i2b2 challenge. aug: au: Xu, Yan Wang, Yining Liu, Tianren Tsujii, Junichi Chang, Eric I-Chao affil: State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, China. sug: subj: Artificial Intelligence Electronic Health Records Information Retrieval Methods Natural Language Processing Patient Discharge Human Research, Medical Time ab: Objective: To create an end-to-end system to identify temporal relation in discharge summaries for the 2012 i2b2 challenge. The challenge includes event extraction, timex extraction, and temporal relation identification.Design: An end-to-end temporal relation system was developed. It includes three subsystems: an event extraction system (conditional random fields (CRF) name entity extraction and their corresponding attribute classifiers), a temporal extraction system (CRF name entity extraction, their corresponding attribute classifiers, and context-free grammar based normalization system), and a temporal relation system (10 multi-support vector machine (SVM) classifiers and a Markov logic networks inference system) using labeled sequential pattern mining, syntactic structures based on parse trees, and results from a coordination classifier. Micro-averaged precision (P), recall (R), averaged P&R (P&R), and F measure (F) were used to evaluate results.Results: For event extraction, the system achieved 0.9415 (P), 0.8930 (R), 0.9166 (P&R), and 0.9166 (F). The accuracies of their type, polarity, and modality were 0.8574, 0.8585, and 0.8560, respectively. For timex extraction, the system achieved 0.8818, 0.9489, 0.9141, and 0.9141, respectively. The accuracies of their type, value, and modifier were 0.8929, 0.7170, and 0.8907, respectively. For temporal relation, the system achieved 0.6589, 0.7129, 0.6767, and 0.6849, respectively. For end-to-end temporal relation, it achieved 0.5904, 0.5944, 0.5921, and 0.5924, respectively. With the F measure used for evaluation, we were ranked first out of 14 competing teams (event extraction), first out of 14 teams (timex extraction), third out of 12 teams (temporal relation), and second out of seven teams (end-to-end temporal relation).Conclusions: The system achieved encouraging results, demonstrating the feasibility of the tasks defined by the i2b2 organizers. The experiment result demonstrates that both global and local information is useful in the 2012 challenge. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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