Deep neural networks ensemble for detecting medication mentions in tweets. (27th September 2019)

• Record Type: Journal Article
• Authors: Weissenbacher, Davy; Sarker, Abeed; Klein, Ari; O'Connor, Karen; Magge, Arjun; Gonzalez-Hernandez, Graciela
• Is Part Of: Journal of the American Medical Informatics Association. Volume 26:Number 12(2019)
• Page Start: 1618
• Page End: 1626
• Publication Date: 2019-09-27
• Subjects: social media -- pharmacovigilance -- drug name detection -- ensemble learning -- text classification; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocz156
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Twitter posts are now recognized as an important source of patient-generated data, providing unique insights into population health. A fundamental step toward incorporating Twitter data in pharmacoepidemiologic research is to automatically recognize medication mentions in tweets. Given that lexical searches for medication names suffer from low recall due to misspellings or ambiguity with common words, we propose a more advanced method to recognize them. Materials and Methods: We present Kusuri, an Ensemble Learning classifier able to identify tweets mentioning drug products and dietary supplements. Kusuri (薬, "medication" in Japanese) is composed of 2 modules: first, 4 different classifiers (lexicon based, spelling variant based, pattern based, and a weakly trained neural network) are applied in parallel to discover tweets potentially containing medication names; second, an ensemble of deep neural networks encoding morphological, semantic, and long-range dependencies of important words in the tweets makes the final decision. Results: On a class-balanced (50-50) corpus of 15 005 tweets, Kusuri demonstrated performances close to human annotators with an F1 score of 93.7%, the best score achieved thus far on this corpus. On a corpus made of all tweets posted by 112 Twitter users (98 959 tweets, with only 0.26% mentioning medications), Kusuri obtained an F1 score of 78.8%. To the best of our knowledge, Kusuri is the first system to achieve this score on such an extremely imbalanced dataset. Conclusions: The system identifies tweets mentioning drug names with performance high enough to ensure its usefulness, and is ready to be integrated in pharmacovigilance, toxicovigilance, or more generally, public health pipelines that depend on medication name mentions.