Mining Adverse Drug Reactions in Social Media with Named Entity Recognition and Semantic Methods. - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Studies in Health Technology and Informatics Année : 2017

Mining Adverse Drug Reactions in Social Media with Named Entity Recognition and Semantic Methods.

Xiaoyi Chen
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  • IdHAL : xiaoyi-chen
Myrtille Deldossi
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Rim Aboukhamis
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Carole Faviez
Armelle Guenegou-Arnoux
Yannick Girardeau
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Sylvie Guillemin-Lanne
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Agnès Lillo-Le-Louët
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Nathalie Texier
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Résumé

Suspected adverse drug reactions (ADR) reported by patients through social media can be a complementary source to current pharmacovigilance systems. However, the performance of text mining tools applied to social media text data to discover ADRs needs to be evaluated. In this paper, we introduce the approach developed to mine ADR from French social media. A protocol of evaluation is highlighted, which includes a detailed sample size determination and evaluation corpus constitution. Our text mining approach provided very encouraging preliminary results with F-measures of 0.94 and 0.81 for recognition of drugs and symptoms respectively, and with F-measure of 0.70 for ADR detection. Therefore, this approach is promising for downstream pharmacovigilance analysis.
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Dates et versions

hal-03251581 , version 1 (07-06-2021)

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Citer

Xiaoyi Chen, Myrtille Deldossi, Rim Aboukhamis, Carole Faviez, Badisse Dahamna, et al.. Mining Adverse Drug Reactions in Social Media with Named Entity Recognition and Semantic Methods.. Studies in Health Technology and Informatics, 2017, 245, pp.322-326. ⟨10.3233/978-1-61499-830-3-322⟩. ⟨hal-03251581⟩
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