Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents.
Unlabelled: OBJECTIVE To describe a new medication information extraction system-Textractor-developed for the 'i2b2 medication extraction challenge'. The development, functionalities, and official evaluation of the system are detailed.Design: Textractor is based on the Apache Unstructured Informatio...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 17; no. 5; pp. 559 - 563 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Sep2010
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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=105091999&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105091999 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: Sep2010 vid: 17 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 105091999 NLM20819864 2010773956 10.1136/jamia.2010.004028 NLM20819864 PMC2995680 105091999 ppf: 559 ppct: 4 formats: tig: atl: Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents. aug: au: Meystre SM Thibault J Shen S Hurdle JF South BR Meystre, Stéphane M Thibault, Julien Shen, Shuying Hurdle, John F South, Brett R affil: Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA sug: subj: Electronic Health Records Information Retrieval Methods Natural Language Processing Drugs Artificial Intelligence Human Information Science ab: Unlabelled: OBJECTIVE To describe a new medication information extraction system-Textractor-developed for the 'i2b2 medication extraction challenge'. The development, functionalities, and official evaluation of the system are detailed.Design: Textractor is based on the Apache Unstructured Information Management Architecture (UMIA) framework, and uses methods that are a hybrid between machine learning and pattern matching. Two modules in the system are based on machine learning algorithms, while other modules use regular expressions, rules, and dictionaries, and one module embeds MetaMap Transfer.Measurements: The official evaluation was based on a reference standard of 251 discharge summaries annotated by all teams participating in the challenge. The metrics used were recall, precision, and the F(1)-measure. They were calculated with exact and inexact matches, and were averaged at the level of systems and documents.Results: The reference metric for this challenge, the system-level overall F(1)-measure, reached about 77% for exact matches, with a recall of 72% and a precision of 83%. Performance was the best with route information (F(1)-measure about 86%), and was good for dosage and frequency information, with F(1)-measures of about 82-85%. Results were not as good for durations, with F(1)-measures of 36-39%, and for reasons, with F(1)-measures of 24-27%.Conclusion: The official evaluation of Textractor for the i2b2 medication extraction challenge demonstrated satisfactory performance. This system was among the 10 best performing systems in this challenge. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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