Abstract
Despite advances in concept extraction from free text, finding meaningful health related information from online patient forums still poses a significant challenge. Here we demonstrate how structured information can be extracted from posts found in such online health related forums by forming relationships between a drug/treatment and a symptom or side effect, including the polarity/sentiment of the patient. In particular, a rule-based natural language processing (NLP) system is deployed, where information in sentences is linked together though anaphora resolution. Our NLP relationship extraction system provides a strong baseline, achieving an \(\text {F}_1\) score of over 80% in discovering the said relationships that are present in the posts we analysed.
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References
Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(suppl 1), D267–D270 (2004)
Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V., Aswani, N., Roberts, I., Gorrell, G., Funk, A., Roberts, A., Damljanovic, D., et al.: Developing language processing components with gate version 6 (a user guide). University of Sheffield, Department of Computer Science (2011)
Dai, H.J., Touray, M., Jonnagaddala, J., Syed-Abdul, S.: Feature engineering for recognizing adverse drug reactions from twitter posts. Information 7(2), 27 (2016)
DailyStrength: https://www.dailystrength.org/. Accessed 04 May 2017
Denecke, K., Deng, Y.: Sentiment analysis in medical settings: new opportunities and challenges. Artif. Intell. Med. 64(1), 17–27 (2015)
Gooch, P., Roudsari, A.: Lexical patterns, features and knowledge resources for coreference resolution in clinical notes. J. Biomed. Inform. 45(5), 901–912 (2012)
Gupta, S., MacLean, D.L., Heer, J., Manning, C.D.: Induced lexico-syntactic patterns improve information extraction from online medical forums. J. Am. Med. Inf. Assoc. 21(5), 902–909 (2014)
Karimi, S., Wang, C., Metke-Jimenez, A., Gaire, R., Paris, C.: Text and data mining techniques in adverse drug reaction detection. ACM Comput. Surv. (CSUR) 47(4), 56 (2015)
Korkontzelos, I., Nikfarjam, A., Shardlow, M., Sarker, A., Ananiadou, S., Gonzalez, G.H.: Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts. J. Biomed. Inform. 62, 148–158 (2016)
Manning, C.D., Schütze, H., et al.: Foundations of Statistical Natural Language Processing, vol. 999. MIT Press, Cambridge (1999)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
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. 22, 1–11 (2015)
Pain, J., Levacher, J., Quinqunel, A., Belz, A.: Analysis of twitter data for postmarketing surveillance in pharmacovigilance. In: Proceedings of the 2nd Workshop on Noisy User-generated Text, pp. 94–101 (2016)
PatientsLikeMe: https://www.patientslikeme.com/. Accessed 21 Apr 2017
Polanyi, L., Zaenen, A.: Contextual valence shifters. In: Shanahan, J.G., Qu, Y., Wiebe, J. (eds.) Computing Attitude and Affect in Text: Theory and Applications. The Information Retrieval Series, vol. 20, pp. 1–10. Springer, Dordrecht (2006). doi:10.1007/1-4020-4102-0_1
Sampathkumar, H., Chen, X.W., Luo, B.: Mining adverse drug reactions from online healthcare forums using hidden markov model. BMC Med. Inform. Decis. Making 14(1), 91 (2014)
U.S. National Library of Medicine: https://www.nlm.nih.gov/. Accessed 21 Jun 2016
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pp. 347–354. Association for Computational Linguistics (2005)
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)
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Hasan, A., Levene, M., Weston, D.J. (2017). Natural Language Analysis of Online Health Forums. In: Adams, N., Tucker, A., Weston, D. (eds) Advances in Intelligent Data Analysis XVI. IDA 2017. Lecture Notes in Computer Science(), vol 10584. Springer, Cham. https://doi.org/10.1007/978-3-319-68765-0_11
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DOI: https://doi.org/10.1007/978-3-319-68765-0_11
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