Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning.
Objective: Automated analysis of vaccine postmarketing surveillance narrative reports is important to understand the progression of rare but severe vaccine adverse events (AEs). This study implemented and evaluated state-of-the-art deep learning algorithms for named entity recognition to extract ner...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 28; no. 7; pp. 1393 - 1401 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jul2021
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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=151400455&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151400455 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: Jul2021 vid: 28 iid: 7 pid: 622 pub: Oxford University Press / USA artinfo: ui: 151400455 151400455 NLM33647938 151400455 10.1093/jamia/ocab014 NLM33647938 151400455 ppf: 1393 ppct: 8 formats: tig: atl: Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning. aug: au: Du, Jingcheng Xiang, Yang Sankaranarayanapillai, Madhuri Zhang, Meng Wang, Jingqi Si, Yuqi Pham, Huy Anh Xu, Hua Chen, Yong Tao, Cui affil: School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, Texas, USA sug: subj: Guillain-Barre Syndrome Influenza Vaccine Adverse Effects Adverse Drug Event United States Computer Systems Funding Source Human ab: Objective: Automated analysis of vaccine postmarketing surveillance narrative reports is important to understand the progression of rare but severe vaccine adverse events (AEs). This study implemented and evaluated state-of-the-art deep learning algorithms for named entity recognition to extract nervous system disorder-related events from vaccine safety reports.Materials and Methods: We collected Guillain-Barré syndrome (GBS) related influenza vaccine safety reports from the Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016. VAERS reports were selected and manually annotated with major entities related to nervous system disorders, including, investigation, nervous_AE, other_AE, procedure, social_circumstance, and temporal_expression. A variety of conventional machine learning and deep learning algorithms were then evaluated for the extraction of the above entities. We further pretrained domain-specific BERT (Bidirectional Encoder Representations from Transformers) using VAERS reports (VAERS BERT) and compared its performance with existing models.Results and Conclusions: Ninety-one VAERS reports were annotated, resulting in 2512 entities. The corpus was made publicly available to promote community efforts on vaccine AEs identification. Deep learning-based methods (eg, bi-long short-term memory and BERT models) outperformed conventional machine learning-based methods (ie, conditional random fields with extensive features). The BioBERT large model achieved the highest exact match F-1 scores on nervous_AE, procedure, social_circumstance, and temporal_expression; while VAERS BERT large models achieved the highest exact match F-1 scores on investigation and other_AE. An ensemble of these 2 models achieved the highest exact match microaveraged F-1 score at 0.6802 and the second highest lenient match microaveraged F-1 score at 0.8078 among peer models. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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