Precursor-induced conditional random fields: connecting separate entities by induction for improved clinical named entity recognition.
Background: This paper presents a conditional random fields (CRF) method that enables the capture of specific high-order label transition factors to improve clinical named entity recognition performance. Consecutive clinical entities in a sentence are usually separated from each other, and the textu...
| Published in: | BMC Medical Informatics & Decision Making Vol. 19; no. 1 |
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| Main Authors: | , |
| Format: | research Journal Article |
| Published: |
BioMed Central
7/15/2019
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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=137489912&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137489912 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 7/15/2019 vid: 19 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 137489912 137489912 NLM31307440 137489912 10.1186/s12911-019-0865-1 NLM31307440 137489912 ppct: 1 formats: tig: atl: Precursor-induced conditional random fields: connecting separate entities by induction for improved clinical named entity recognition. aug: au: Lee, Wangjin Choi, Jinwook affil: Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University, 103 Daehak-ro, Jongno-gu, 03080, Seoul, South Korea sug: subj: Natural Language Processing Health Information Systems Models, Theoretical Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales ab: Background: This paper presents a conditional random fields (CRF) method that enables the capture of specific high-order label transition factors to improve clinical named entity recognition performance. Consecutive clinical entities in a sentence are usually separated from each other, and the textual descriptions in clinical narrative documents frequently indicate causal or posterior relationships that can be used to facilitate clinical named entity recognition. However, the CRF that is generally used for named entity recognition is a first-order model that constrains label transition dependency of adjoining labels under the Markov assumption.Methods: Based on the first-order structure, our proposed model utilizes non-entity tokens between separated entities as an information transmission medium by applying a label induction method. The model is referred to as precursor-induced CRF because its non-entity state memorizes precursor entity information, and the model's structure allows the precursor entity information to propagate forward through the label sequence.Results: We compared the proposed model with both first- and second-order CRFs in terms of their F1-scores, using two clinical named entity recognition corpora (the i2b2 2012 challenge and the Seoul National University Hospital electronic health record). The proposed model demonstrated better entity recognition performance than both the first- and second-order CRFs and was also more efficient than the higher-order model.Conclusion: The proposed precursor-induced CRF which uses non-entity labels as label transition information improves entity recognition F1 score by exploiting long-distance transition factors without exponentially increasing the computational time. In contrast, a conventional second-order CRF model that uses longer distance transition factors showed even worse results than the first-order model and required the longest computation time. Thus, the proposed model could offer a considerable performance improvement over current clinical named entity recognition methods based on the CRF models. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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