Akhtyamova, L., Alexandrov, M., & Cardiff, J. (2017a). Adverse drug extraction in twitter data using convolutional neural network. In, 2017 28th International Workshop on Database and Expert Systems Applications (DEXA) (pp. 88–92). Lyon.
DOI: https://doi.org/10.1109/DEXA.2017.34
Akhtyamova, L., Ignatov, A., & Cardiff, J. (2017b). A Large-scale CNN ensemble for medication safety analysis. In F. Frasincar, A. Ittoo, L. Nguyen & E. Métais (Eds.) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science (vol. 10260, pp. 247–253). Springer, Cham.
DOI: https://doi.org/10.1007/978-3-319-59569-6_29
Bordet, R., Gautier, S., Louet, H. L., Dupuis, B., & Caron, J. (2001). Analysis of the direct cost of adverse drug reactions in hospitalised patients. European journal of clinical pharmacology, 56(12), 935–941.
DOI: https://doi.org/10.1007/s002280000260
Classen, D. C., Pestotnik, S. L., Evans, R. S., Lloyd, J.F., & Burke, J. P. (1997). Adverse drug events in hospitalized patients: excess length of stay, extra costs, and attributable mortality. Jama, 277(4), 301–306.
DOI: https://doi.org/10.1001/jama.277.4.301
Ginn, R., Pimpalkhute, P., Nikfarjam, A., Patki, A., O’Connor, K., Sarker, A., Smith, K., & Gonzalez, G. (2014). Mining Twitter for adverse drug reaction mentions, a corpus and classification benchmark. In Proceedings of the fourth workshop on building and evaluating resources for health and biomedical text processing (pp. 1–8).
Gupta, S., Pawar, S., Ramrakhiyani, N., Palshikar, G. K., & Varma, V. (2018). Semi-supervised recurrent neural network for adverse drug reaction mention extraction. BMC bioinformatics, 19(8), 212.
DOI: https://doi.org/10.1186/s12859-018-2192-4
Harpaz, R., Callahan, A., Tamang, S., Low, Y., Odgers, D., Finlayson, S., Jung, K., LePendu, P., & Shah, N. H. (2014). Text mining for adverse drug events, the promise, challenges, and state of the art. Drug safety, 37(10), 777–790.
DOI: https://doi.org/10.1007/s40264-014-0218-z
Ho, T. B., Le, L., Thai, D. T., & Taewijit, S. (2016). Data-driven approach to detect and predict adverse drug reactions. Current pharmaceutical design, 22(23), 3498–3526.
DOI: https://doi.org/10.2174/1381612822666160509125047
Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016). Bag of tricks for efficient text classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers (pp. 427–431). Association for Computational Linguistics.
DOI: https://doi.org/10.18653/v1/E17-2068
Kongkaew, C., Noyce, P. R., & Ashcroft, D.M. (2008). Hospital admissions associated with adverse drug reactions: a systematic review of prospective observational studies. Annals of Pharmacotherapy, 42(7–8), 1017–1025.
DOI: https://doi.org/10.1345/aph.1L037
Lee, K., Qadir, A., Hasan, S. A., Datla, V., Prakash, A., Liu, J., & Farri, O. (2017). Adverse drug event detection in tweets with semi-supervised convolutional neural networks. In Proceedings of the 26th International Conference on World Wide Web (pp. 705–714). Republic and Canton of Geneva, CHE: International World Wide Web Conferences Steering Committee. http://doi.org/10.1145/3038912.3052671.
DOI: https://doi.org/10.1145/3038912.3052671
Miranda, D. S. (2018). Automated detection of adverse drug reactions in the biomedical literature using convolutional neural networks and biomedical word embeddings. SwissText.
Rezaei, Z., Ebrahimpour-Komleh, H., Eslami, B., Chavoshinejad, R., & Totonchi, M. (2020). Adverse Drug Reaction Detection in Social Media by Deepm Learning Methods. Cell journal, 22(3), 319–324.
Rison, R. A. (2013). A guide to writing case reports. Journal of Medical Case Reports and BioMed Central Research Notes, 7, 239. http://doi.org/10.1186/1752-1947-7-239
DOI: https://doi.org/10.1186/1752-1947-7-239
Sarker, A., & Gonzalez, G. (2015). Portable automatic text classification for adverse drug reaction detection via multi-corpus training. Journal of biomedical informatics, 53, 196–207.
DOI: https://doi.org/10.1016/j.jbi.2014.11.002
Sultana, J., Cutroneo, P., & Trifirò, G. (2013). Clinical and economic burden of adverse drug reactions. Journal of pharmacology, 4(Suppl1), 73.
DOI: https://doi.org/10.4103/0976-500X.120957
Tan, Y., Hu, Y., Liu, X., Yin, Z., wen Chen, X., & Liu, M. (2016). Improving drug safety, From adverse drug reaction knowledge discovery to clinical implementation. Methods, 110, 14–25.
DOI: https://doi.org/10.1016/j.ymeth.2016.07.023
Vallano, A., Cereza, G., Pedròs, C., Agustí, A., Danés, I., Aguilera, C., & Arnau, J. M. (2005). Obstacles and solutions for spontaneous reporting of adverse drug reactions in the hospital. British journal of clinical pharmacology, 60(6), 653–658.
DOI: https://doi.org/10.1111/j.1365-2125.2005.02504.x
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics, Human Language Technologies (pp. 1480–1489). Association for Computational Linguistics.
DOI: https://doi.org/10.18653/v1/N16-1174
