Exploring Linguistic and Graph Based Features for the Automatic Classification and Extraction of Adverse Drug Effects (original) (raw)
References
Bate, A., et al.: A Bayesian neural network method for adverse drug reaction signal generation. Eur. J. Clin. Pharmacol. 54(4), 315–321 (1998) Article Google Scholar
Bisgin, H., Liu, Z., Fang, H., Xu, X., Tong, W.: Mining FDA drug labels using an unsupervised learning technique-topic modeling. BMC Bioinform. 12(10), S11 (2011) Article Google Scholar
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011) Google Scholar
Chirawichitchai, N., Sa-nguansat, P., Meesad, P.: Developing an effective Thai document categorization framework base on term relevance frequency weighting. In: 2010 8th International Conference on ICT Knowledge Engineering, pp. 19–23. IEEE (2010) Google Scholar
De Marneffe, M.-C., Manning, C.D.: The Stanford typed dependencies representation. In: Coling 2008: Proceedings of the Workshop on Cross-Framework and Cross-Domain Parser Evaluation, pp. 1–8. Association for Computational Linguistics (2008) Google Scholar
Duan, K.-B., Rajapakse, J.C., Wang, H., Azuaje, F.: Multiple SVM-RFE for gene selection in cancer classification with expression data. IEEE Trans. Nanobiosci. 4(3), 228–234 (2005) Article Google Scholar
DuMouchel, W.: Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system. Am. Stat. 53(3), 177–190 (1999) Google Scholar
Eshleman, R., Singh, R.: Leveraging graph topology and semantic context for pharmacovigilance through Twitter-streams. BMC Bioinform. 17(13), 335 (2016) Article Google Scholar
Gamon, M.: Graph-based text representation for novelty detection. In: Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing, pp. 17–24. Association for Computational Linguistics (2006) Google Scholar
Ginn, R., et al.: 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. Citeseer (2014) Google Scholar
Gurulingappa, H., Mateen-Rajpu, A., Toldo, L.: Extraction of potential adverse drug events from medical case reports. J. Biomed. Semant. 3(1), 15 (2012) Article Google Scholar
Gurulingappa, H., et al.: Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports. J. Biomed. Inform. 45(5), 885–892 (2012) Article Google Scholar
Huynh, T., He, Y., Willis, A., Rüger, S.: Adverse drug reaction classification with deep neural networks (2016) Google Scholar
Lazarou, J., Pomeranz, B.H., Corey, P.N.: Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 279(15), 1200–1205 (1998) Article Google Scholar
Lindquist, M., Edwards, I.R., Bate, A., Fucik, H., Nunes, A.M., Ståhl, M.: From association to alert–a revised approach to international signal analysis. Pharmacoepidemiol. Drug Saf. 8(S1), S15–S25 (1999) Article Google Scholar
Liu, M., et al.: Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records. J. Am. Med. Inform. Assoc. 20(3), 420–426 (2013) Article Google Scholar
Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. Association for Computational Linguistics (2004) Google Scholar
Nikfarjam, A., Sarker, A., O’Connor, K., Ginn, R., Gonzalez, G.: Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J. Am. Med. Inform. Assoc. (2015). https://doi.org/10.1093/jamia/ocu041
Pirmohamed, M., et al.: Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. BMJ 329(7456), 15–19 (2004) Article Google Scholar
Rastegar-Mojarad, M., Komandur Elayavilli, R., Yu, Y., Hiu, H.: Detecting signals in noisy data-can ensemble classifiers help identify adverse drug reaction in tweets. In: Proceedings of the Social Media Mining Shared Task Workshop at the Pacific Symposium on Biocomputing (2016) Google Scholar
Sarker, A., Nikfarjam, A., Gonzalez, G.: Social media mining shared task workshop. In: Proceedings of the Pacific Symposium on Biocomputing, pp. 581–592 (2016) Google Scholar
Szarfman, A., Machado, S.G., O’Neill, R.T.: Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the us FDA’s spontaneous reports database. Drug Saf. 25(6), 381–392 (2002) Article Google Scholar
Wang, X., Hripcsak, G., Markatou, M., Friedman, C.: Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J. Am. Med. Inform. Assoc. 16(3), 328–337 (2009) Article Google Scholar
Zhang, Z., Nie, J., Zhang, X.: An ensemble method for binary classification of adverse drug reactions from social media. In: Proceedings of the Social Media Mining Shared Task Workshop at the Pacific Symposium on Biocomputing (2016) Google Scholar