J Am Med Inform Assoc - Vaccine adverse event text mining system for extracting features from vaccine safety reports.

Tópicos

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Resumo

JECTIVE: To develop and evaluate a text mining system for extracting key clinical features from vaccine adverse event reporting system (VAERS) narratives to aid in the automated review of adverse event reports.DESIGN: Based upon clinical significance to VAERS reviewing physicians, we defined the primary (diagnosis and cause of death) and secondary features (eg, symptoms) for extraction. We built a novel vaccine adverse event text mining (VaeTM) system based on a semantic text mining strategy. The performance of VaeTM was evaluated using a total of 300 VAERS reports in three sequential evaluations of 100 reports each. Moreover, we evaluated the VaeTM contribution to case classification; an information retrieval-based approach was used for the identification of anaphylaxis cases in a set of reports and was compared with two other methods: a dedicated text classifier and an online tool.MEASUREMENTS: The performance metrics of VaeTM were text mining metrics: recall, precision and F-measure. We also conducted a qualitative difference analysis and calculated sensitivity and specificity for classification of anaphylaxis cases based on the above three approaches.RESULTS: VaeTM performed best in extracting diagnosis, second level diagnosis, drug, vaccine, and lot number features (lenient F-measure in the third evaluation: 0.897, 0.817, 0.858, 0.874, and 0.914, respectively). In terms of case classification, high sensitivity was achieved (83.1%); this was equal and better compared to the text classifier (83.1%) and the online tool (40.7%), respectively.CONCLUSION: Our VaeTM implementation of a semantic text mining strategy shows promise in providing accurate and efficient extraction of key features from VAERS narratives.

Resumo Limpo

jectiv develop evalu text mine system extract key clinic featur vaccin advers event report system vaer narrat aid autom review advers event reportsdesign base upon clinic signific vaer review physician defin primari diagnosi caus death secondari featur eg symptom extract built novel vaccin advers event text mine vaetm system base semant text mine strategi perform vaetm evalu use total vaer report three sequenti evalu report moreov evalu vaetm contribut case classif inform retrievalbas approach use identif anaphylaxi case set report compar two method dedic text classifi onlin toolmeasur perform metric vaetm text mine metric recal precis fmeasur also conduct qualit differ analysi calcul sensit specif classif anaphylaxi case base three approachesresult vaetm perform best extract diagnosi second level diagnosi drug vaccin lot number featur lenient fmeasur third evalu respect term case classif high sensit achiev equal better compar text classifi onlin tool respectivelyconclus vaetm implement semant text mine strategi show promis provid accur effici extract key featur vaer narrat

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