Named entity recognition of follow-up and time information in 20,000 radiology reports.
Objective: To develop a system to extract follow-up information from radiology reports. The method may be used as a component in a system which automatically generates follow-up information in a timely fashion.Methods: A novel method of combining an LSP (labeled sequential pattern) classifier with a...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 19; no. 5; pp. 792 - 800 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Sep2012
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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=104360937&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104360937 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: Sep2012 vid: 19 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104360937 NLM22771530 2011646706 10.1136/amiajnl-2012-000812 NLM22771530 PMC3422839 104360937 ppf: 792 ppct: 8 formats: tig: atl: Named entity recognition of follow-up and time information in 20,000 radiology reports. aug: au: Xu, Yan 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: Data Mining Methods Electronic Health Records Classification Natural Language Processing Radiology Information Systems Artificial Intelligence Pilot Studies Human ab: Objective: To develop a system to extract follow-up information from radiology reports. The method may be used as a component in a system which automatically generates follow-up information in a timely fashion.Methods: A novel method of combining an LSP (labeled sequential pattern) classifier with a CRF (conditional random field) recognizer was devised. The LSP classifier filters out irrelevant sentences, while the CRF recognizer extracts follow-up and time phrases from candidate sentences presented by the LSP classifier.Measurements: The standard performance metrics of precision (P), recall (R), and F measure (F) in the exact and inexact matching settings were used for evaluation.Results: Four experiments conducted using 20,000 radiology reports showed that the CRF recognizer achieved high performance without time-consuming feature engineering and that the LSP classifier further improved the performance of the CRF recognizer. The performance of the current system is P=0.90, R=0.86, F=0.88 in the exact matching setting and P=0.98, R=0.93, F=0.95 in the inexact matching setting.Conclusion: The experiments demonstrate that the system performs far better than a baseline rule-based system and is worth considering for deployment trials in an alert generation system. The LSP classifier successfully compensated for the inherent weakness of CRF, that is, its inability to use global information. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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