Adverse drug event detection using reason assignments in FDA drug labels.
Adverse drug events (ADEs) are unintended incidents that involve the taking of a medication. ADEs pose significant health and financial problems worldwide. Information about ADEs can inform health care and improve patient safety. However, much of this information is buried in narrative texts and nee...
| Publicado en: | Journal of Biomedical Informatics Vol. 110 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Academic Press Inc.
Oct2020
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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=146481887&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146481887 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Oct2020 vid: 110 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 146481887 146481887 NLM32890727 10.1016/j.jbi.2020.103552 NLM32890727 146481887 ppct: 1 formats: tig: atl: Adverse drug event detection using reason assignments in FDA drug labels. aug: au: Sutphin, Corey Lee, Kahyun Yepes, Antonio Jimeno Uzuner, Özlem McInnes, Bridget T. affil: Virginia Commonwealth University, Richmond, VA, USA sug: subj: Adverse Drug Event Drugs Drug Labeling Natural Language Processing Short Portable Mental Status Questionnaire Clinical Assessment Tools Barthel Index ab: Adverse drug events (ADEs) are unintended incidents that involve the taking of a medication. ADEs pose significant health and financial problems worldwide. Information about ADEs can inform health care and improve patient safety. However, much of this information is buried in narrative texts and needs to be extracted with Natural Language Processing techniques, in order to be useful to computerized methods. ADEs can be found on drug labels, contained in the different sections such as descriptions of the drug's active components or more prominently in descriptions of studied side-effects. Extracting these automatically could be useful in triaging and processing drug reports. In this paper, we present three base methods consisting of a Conditional Random Field (CRF), a bi-directional Long Short Term Memory unit with a CRF layer (biLSTM+CRF), and a pre-trained Bi-directional Encoder Representations from Transformers (BERT) model. We also present several ensembles of the CRF and biLSTM+CRF methods for extracting ADEs and their Reason from FDA drug labels. We show that all three methods perform well on our task, and that combining the models through different ensemble methods can improve results, providing increases in recall for the majority class and improving precision for all other classes. We also show the potential of framing ADE extraction from drug labels as a multi-class classification task on the Reason, or type, of ADE. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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