Abstract
The WHO indicates that the adverse drug effects are of great relevance as each person has different reactions regardless of whether the dose or treatment is correct, affecting their health. Unfortunately, in the literature, there are very few methodologies for early detection of these ADRs, specifically in the Spanish language, and the existing methods deal with them from medical records, leaving aside a great source of information such as social networks. In this work, we present the creation of a corpus in Spanish with data from Twitter. The realization of experiments for evaluating the corpus with a machine learning algorithm SVM and two neural network models RAM and IAN. The best results were obtained with an accuracy of 0.86 with the RAM neural network.
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Acknowledgements
Al Consejo Nacional de Ciencia y Tecnología (CONACYT) por la beca otorgada para realizar estudios de posgrado a nivel maestría a través de la convocatoria “BECAS NACIONALES PARA ESTUDIOS DE POSGRADO 2022”.
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Montero-Colio, M.G., Salas-Zárate, M.d.P., Paredes-Valverde, M.A. (2023). A First Approach to the Classification of Adverse Drug Effects on Twitter Through Machine Learning. In: Valencia-García, R., Bucaram-Leverone, M., Del Cioppo-Morstadt, J., Vera-Lucio, N., Centanaro-Quiroz, P.H. (eds) Technologies and Innovation. CITI 2023. Communications in Computer and Information Science, vol 1873. Springer, Cham. https://doi.org/10.1007/978-3-031-45682-4_8
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