Adverse Drug Events Detection in Clinical Notes by Jointly Modeling Entities and Relations Using Neural Networks.
Background and Significance: Adverse drug events (ADEs) occur in approximately 2-5% of hospitalized patients, often resulting in poor outcomes or even death. Extraction of ADEs from clinical narratives can accelerate and automate pharmacovigilance. Using state-of-the-art deep-learning neural network...
| Publicado en: | Drug Safety Vol. 42; no. 1; pp. 135 - 147 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2019
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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=134584695&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134584695 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01145916 C5E jtl: Drug Safety issn: 01145916 maglogo: N pubinfo: dt: Jan2019 vid: 42 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134584695 134584695 NLM30649738 10.1007/s40264-018-0764-x NLM30649738 134584695 ppf: 135 ppct: 12 formats: tig: atl: Adverse Drug Events Detection in Clinical Notes by Jointly Modeling Entities and Relations Using Neural Networks. aug: au: Dandala, Bharath Joopudi, Venkata Devarakonda, Murthy affil: IBM Research, Yorktown Heights, NY, USA sug: subj: Neural Networks (Computer) Adverse Drug Event Epidemiology Adverse Drug Event Trends Adverse Drug Event Adverse Drug Event Standards Clinical Assessment Tools Scales Short Portable Mental Status Questionnaire ab: Background and Significance: Adverse drug events (ADEs) occur in approximately 2-5% of hospitalized patients, often resulting in poor outcomes or even death. Extraction of ADEs from clinical narratives can accelerate and automate pharmacovigilance. Using state-of-the-art deep-learning neural networks to jointly model concept and relation extraction, we achieved the highest integrated task score in the 2018 Medication and Adverse Drug Event (MADE) 1.0 challenge.Methods: We used a combined bidirectional long short-term memory (BiLSTM) and conditional random fields (CRF) neural network to detect medical entities relevant to ADEs and a combined BiLSTM and attention network to determine relations, including the adverse drug reaction relation between medication and sign or symptom entities. Using these models, we conducted three experiments: (1) separate and sequential modeling of entities and relations; (2) joint modeling where relations between medications and sign or symptoms determined ADE and indication entities; (3) use of information from external resources such as the US FDA's adverse event database as additional input to the second method.Results: Joint modeling improved the overall task accuracy from 0.62 to 0.65 F measure, and the additional use of external resources improved the accuracy to 0.66 F measure. Given the gold-standard medical entity labels, the joint model plus external resources method achieved F measures of 0.83 for ADE-relevant medical entity detection and 0.87 for relation detection.Conclusion: It is important to use joint modeling techniques and external resources for effectively detecting ADEs from clinical narratives in electronic health record (EHR) systems. While the extraction of entities and relations individually achieved high accuracy, the integrated task still has room for further improvement. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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