Lancet: a high precision medication event extraction system for clinical text.
Objective: This paper presents Lancet, a supervised machine-learning system that automatically extracts medication events consisting of medication names and information pertaining to their prescribed use (dosage, mode, frequency, duration and reason) from lists or narrative text in medical discharge...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 17; no. 5; pp. 563 - 568 |
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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=105092000&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105092000 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: 105092000 NLM20819865 2010773957 10.1136/jamia.2010.004077 NLM20819865 PMC2995682 105092000 ppf: 563 ppct: 5 formats: tig: atl: Lancet: a high precision medication event extraction system for clinical text. aug: au: Li Z Liu F Antieau L Cao Y Yu H Li, Zuofeng Liu, Feifan Antieau, Lamont Cao, Yonggang Yu, Hong affil: College of Health Sciences, University of Wisconsin-Milwaukee, Wisconsin, USA sug: subj: Artificial Intelligence Electronic Health Records Information Retrieval Methods Natural Language Processing Human ab: Objective: This paper presents Lancet, a supervised machine-learning system that automatically extracts medication events consisting of medication names and information pertaining to their prescribed use (dosage, mode, frequency, duration and reason) from lists or narrative text in medical discharge summaries.Design: Lancet incorporates three supervised machine-learning models: a conditional random fields model for tagging individual medication names and associated fields, an AdaBoost model with decision stump algorithm for determining which medication names and fields belong to a single medication event, and a support vector machines disambiguation model for identifying the context style (narrative or list).Measurements: The authors, from the University of Wisconsin-Milwaukee, participated in the third i2b2 shared-task for challenges in natural language processing for clinical data: medication extraction challenge. With the performance metrics provided by the i2b2 challenge, the micro F1 (precision/recall) scores are reported for both the horizontal and vertical level.Results: Among the top 10 teams, Lancet achieved the highest precision at 90.4% with an overall F1 score of 76.4% (horizontal system level with exact match), a gain of 11.2% and 12%, respectively, compared with the rule-based baseline system jMerki. By combining the two systems, the hybrid system further increased the F1 score by 3.4% from 76.4% to 79.0%.Conclusions: Supervised machine-learning systems with minimal external knowledge resources can achieve a high precision with a competitive overall F1 score.Lancet based on this learning framework does not rely on expensive manually curated rules. The system is available online at http://code.google.com/p/lancet/. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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