Abstract
Food-drug interactions can profoundly impact desired and adverse effects of drugs, with unexpected and often harmful consequences on the health and well-being of patients. A growing body of scientific publications report clinically relevant food-drug interactions, but conventional search strategies based on handcrafted queries and indexing terms suffer from low recall. In this paper, we introduce a novel task called food-drug interaction discovery that aims to automatically identify scientific publications that describe food-drug interactions from a database of biomedical literature. We make use of an expert curated corpus of food-drug interactions to analyse different methods for query selection and we propose a high-recall approach based on feature selection.
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Acknowledgements
This work was supported by the MIAM project and Agence Nationale de la Recherche through the grant ANR-16-CE23-0012 France.
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Bordea, G., Thiessard, F., Hamon, T., Mougin, F. (2018). Automatic Query Selection for Acquisition and Discovery of Food-Drug Interactions. In: Bellot, P., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2018. Lecture Notes in Computer Science(), vol 11018. Springer, Cham. https://doi.org/10.1007/978-3-319-98932-7_10
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DOI: https://doi.org/10.1007/978-3-319-98932-7_10
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