Abstract
We present the construction of an annotated corpus of PubMed abstracts reporting about positive, negative or neutral effects of treatments or substances. Our ultimate goal is to annotate one sentence (rationale) for each abstract and to use this resource as a training set for text classification of effects discussed in PubMed abstracts. Currently, the corpus consists of 750 abstracts. We describe the automatic processing that supports the corpus construction, the manual annotation activities and some features of the medical language in the abstracts selected for the annotated corpus. It turns out that recognizing the terminology and the abbreviations is key for determining the rationale sentence. The corpus will be applied to improve our classifier, which currently has accuracy of 78.80% achieved with normalization of the abstract terms based on UMLS concepts from specific semantic groups and an SVM with a linear kernel. Finally, we discuss some other possible applications of this corpus.
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References
Zaremba, S., et al.: Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens. BMC Bioinf. 10, 177 (2009)
Doğan, R., Leaman, R., Lu, Z.: NCBI disease corpus: a resource for disease name recognition and concept normalization. J. Biomed. Inf. 47, 1–10 (2014)
Dernoncourt, F., Lee, J.: PubMed 200 k RCT: a dataset for sequential sentence classification in medical abstracts. In: Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP 2017), Taipei, Taiwan, pp. 308–313 (2017)
Zaidan, O., Eisner, J.: Modeling annotators: a generative approach to learning from annotator rationales. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2008), Honolulu, Hawaii, USA, pp. 31–40 (2008)
Lei, T., Barzilay, R., Jaakkola, T.: Rationalizing neural predictions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), Austin, Texas, USA, pp. 107–117 (2016)
Sullivan, R., Yao, R., Jarrar, R., Buchhalter, J., Gonzalez, G.: Text classification towards detecting misdiagnosis of an epilepsy syndrome in a pediatric population. In: Proceedings of the AMIA Annual Symposium, Washington, DC, USA, pp. 1082–1087 (2014)
Chen, Q., Li, H., Tang, B., Wang, X., Liu, X., Liu, Z., Liu, S., Wang, W., Deng, Q., Zhu, S., Chen, Y., Wang, J.: An automatic system to identify heart disease risk factors in clinical texts over time. J. Biomed. Inf. 58, S158–S163 (2015)
Cronin, R., Fabbri, D., Denny, J., Rosenbloom, S., Jackson, G.: A comparison of rule-based and machine learning approaches for classifying patient portal messages. Int. J. Biomed. Inf. 105, 110–120 (2017)
Stefchov, E.: Analysis of biomedical abstracts to determine the effect of treatments and procedures. M.Sc Thesis, Sofia University “St. Kl. Ohridski”, July 2018, in Bulgarian (2018)
Aronson, A.: Effective mapping of biomedical text to the UMLS metathesaurus: the MetaMap program. In: Proceedings of the AMIA Symposium, Washington, DC, USA, pp. 17–21 (2001)
Aronson, A., Lang, F.-M.: An overview of MetaMap: historical perspective and recent advances. J. AMIA 17(3), 229–236 (2010)
Malmasi, S., Hassanzadeh, H., Dras, M.: Clinical information extraction using word representations. In: Proceedings of Australasian Language Technology Association Workshop (ALTA 2015), Parramatta, Australia, pp. 66 − 74 (2015)
Rush, A., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), Lisbon, Portugal, pp. 378–389 (2015)
Mi, H., Wang, Z., Ittycheriah, A.: Supervised attentions for neural machine translation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), Austin, Texas, USA, pp. 2283–2288 (2016)
Malmasi, S., Hassanzadeh, H., Dras, M.: Clinical information extraction using word representations. In: Proceedings of Australasian Language Technology Association Workshop (ALTA 2015), Parramatta, Australia, pp. 66 − 74 (2015)
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The authors are grateful to the anonymous reviewers for their valuable comments.
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Stefchov, E., Angelova, G., Nakov, P. (2018). Towards Constructing a Corpus for Studying the Effects of Treatments and Substances Reported in PubMed Abstracts. In: Agre, G., van Genabith, J., Declerck, T. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2018. Lecture Notes in Computer Science(), vol 11089. Springer, Cham. https://doi.org/10.1007/978-3-319-99344-7_11
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DOI: https://doi.org/10.1007/978-3-319-99344-7_11
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