An Explainable Approach of Inferring Potential Medication Effects from Social Media Data
- 236 Downloads
Understanding medication effects is an important activity in pharmacovigilance in which patients are the most important contributor. Social media, where users share their personal experiences of medication effects, have been recommended as an alternative data source of gathering signal information of suspected medication effects. To discover potential medication-effect relations from Twitter data, we devised a method employing analogical reasoning with neural embedding of Twitter text. The process involves learning the neural embedding from unlabeled tweets and performing vector arithmetic, making it obscure to understand how an inferred relation is derived. To make the process understandable and interpretable and to facilitate the decision making on accepting or rejecting any inferred medication-effect relations, we added explanation(s) to each step of the process. An example of inferred relation is provided to demonstrate the effectiveness of our approach in explaining how the result of each step is derived.
KeywordsPharmacovigilance Medication effects Social media Analogical reasoning Explainable machine learning
Authors wish to thank anonymous reviewers for their critiques and constructive comments that helped improve the final manuscript. This work was supported in part by the US National Institutes of Health Grant 1R15LM011999-01.
The protocol of this project was reviewed and approved for compliance with the human subject research regulation by the Institutional Review Board of Purdue University.
- 4.Levinson, D.R., General, I.: Adverse events in hospitals: national incidence among Medicare beneficiaries. Department of Health & Human Services (2010)Google Scholar
- 7.Mikolov, T., Yih, W.T., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–751 (2013)Google Scholar
- 9.De Marneffe, M.C., MacCartney, B., Manning, C.D.: Generating typed dependency parses from phrase structure parses. LREC 6, 449–454 (2006)Google Scholar
- 11.Google Code Archive, word2vec. https://code.google.com/archive/p/word2vec/
- 13.Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: International Conference on Learning Representations (2013)Google Scholar