A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction.
Objective: The goal of this work was to evaluate machine learning methods, binary classification and sequence labeling, for medication-attribute linkage detection in two clinical corpora.Data and Methods: We double annotated 3000 clinical trial announcements (CTA) and 1655 clinical notes (CN) for me...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 20; no. 5; pp. 915 - 922 |
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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=104086782&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104086782 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: 104086782 NLM23268488 2012222017 10.1136/amiajnl-2012-001487 NLM23268488 PMC3756265 104086782 ppf: 915 ppct: 7 formats: tig: atl: A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction. aug: au: Li, Qi Zhai, Haijun Deleger, Louise Lingren, Todd Kaiser, Megan Stoutenborough, Laura Solti, Imre affil: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. sug: subj: Algorithms Artificial Intelligence Drugs Information Retrieval Methods Medical Records Clinical Trials Human ab: Objective: The goal of this work was to evaluate machine learning methods, binary classification and sequence labeling, for medication-attribute linkage detection in two clinical corpora.Data and Methods: We double annotated 3000 clinical trial announcements (CTA) and 1655 clinical notes (CN) for medication named entities and their attributes. A binary support vector machine (SVM) classification method with parsimonious feature sets, and a conditional random fields (CRF)-based multi-layered sequence labeling (MLSL) model were proposed to identify the linkages between the entities and their corresponding attributes. We evaluated the system's performance against the human-generated gold standard.Results: The experiments showed that the two machine learning approaches performed statistically significantly better than the baseline rule-based approach. The binary SVM classification achieved 0.94 F-measure with individual tokens as features. The SVM model trained on a parsimonious feature set achieved 0.81 F-measure for CN and 0.87 for CTA. The CRF MLSL method achieved 0.80 F-measure on both corpora.Discussion and Conclusions: We compared the novel MLSL method with a binary classification and a rule-based method. The MLSL method performed statistically significantly better than the rule-based method. However, the SVM-based binary classification method was statistically significantly better than the MLSL method for both the CTA and CN corpora. Using parsimonious feature sets both the SVM-based binary classification and CRF-based MLSL methods achieved high performance in detecting medication name and attribute linkages in CTA and CN. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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