Automated Prediction of Demographic Information from Medical User Reviews

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10089)

Abstract

The advent of personalized medicine and wide-scale drug tests has led to the development of methods intended to automatically mine and extract information regarding drug reactions from user reviews. For medical purposes, it is often important to know demographic information on the authors of these reviews; however, existing studies usually either presuppose that this information is available or disregard the issue. We study automatic mining of demographic information from user-generated texts, comparing modern natural language processing techniques, including extensions of topic models and deep neural networks, for this problem on datasets mined from health-related web sites.

Keywords

Topic Model Latent Dirichlet Allocation Convolutional Neural Network Latent Dirichlet Allocation Model Convolutional Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The work is performed according to the Russian Government Program of Competitive Growth of Kazan Federal University. The work of Sergey Nikolenko was also supported by the 2016 grant “User Profiling Based on Distributed Word Representations” sponsored by Samsung. The work of Elena Tutubalina on the collection of two real-world datasets and method implementation was supported by the Russian Science Foundation grant no. 15-11-10019.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Kazan (Volga Region) Federal UniversityKazanRussia
  2. 2.Steklov Institute of MathematicsSt. PetersburgRussia
  3. 3.Laboratory for Internet StudiesNRU Higher School of EconomicsSt. PetersburgRussia

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