Patient Empowerment Through Summarization of Discussion Threads on Treatments in a Patient Self-help Forum

  • Sourabh Dandage
  • Johannes Huber
  • Atin Janki
  • Uli Niemann
  • Ruediger Pryss
  • Manfred Reichert
  • Steve Harrison
  • Markku Vessala
  • Winfried Schlee
  • Thomas Probst
  • Myra Spiliopoulou
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 66)

Abstract

Self-help patient fora are widely used for information acquisition and exchange of experiences, e.g., on the effects of medical treatments for a disease. However, a new patient may have difficulties in getting a fast overview of the information inside a large forum. We propose TinnitusTreatmentMonitor, a prototype tool for the summarization and sentiment characterization of postings on medical treatments. We report on applying TinnitusTreatmentMonitor on the platform TinnitusTalk, a self-help platform for tinnitus patients.

Keywords

Self-help patient fora Opinions on treatments Discussion threads Sentiment analysis Medical mining 

Notes

Acknowledgements

Partly, the work done by U. Niemann and M. Spiliopoulou was within the German Research Foundation project OSCAR ``Opinion Stream Classification with Ensembles and Active Learners’’: U. Niemann is partially funded by OSCAR, whereas M. Spiliopoulou is project investigator.

Compliance with Ethical Standards

The authors declare that they have no conflict of interest and no conflict with ethical standards. The social platform is public domain.

References

  1. 1.
    Baguley D, McFerran D (2013) Tinnitus. Lancet 382:1600–1607CrossRefGoogle Scholar
  2. 2.
    Thomas Probst et al (2017) Outpatient tinnitus clinic, self-help web platform, or mobile application to recruit tinnitus study samples? Front Aging Neurosci 9:113CrossRefGoogle Scholar
  3. 3.
    Jens Türp, Harald Ohla (2012) Temporomandibular joint pain: analyzing discussions in online forums. Zeitschrift für Kraniomandibuläre Funktion 4:227–244Google Scholar
  4. 4.
    Liu X, Chen H (2013) AZDrugMiner: an information extraction system for mining patient-reported adverse drug events in online patient forums. Springer, Berlin, pp 134–150Google Scholar
  5. 5.
    Korkontzelos I et al (2016) Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts. J Biomed Inf 62:148–158CrossRefGoogle Scholar
  6. 6.
    Lorraine G et al (2012) Sentiment lexicons for health-related opinion mining. In: Proceedings of the 2nd ACM SIGHIT Int’l health informatics symposium. ACM, pp 219–226Google Scholar
  7. 7.
    Asghar DM et al (2013) Health miner: opinion extraction from user generated health reviews, vol 5, pp 279–284Google Scholar
  8. 8.
    Bird S et al (2009) Natural language processing with python. O’Reilly, 1st edn 9Google Scholar
  9. 9.
    Pedregosa F et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830MATHMathSciNetGoogle Scholar
  10. 10.
    McKinney W (2012) Python for data analysis. O’Reilly, 1st ednGoogle Scholar
  11. 11.
    Bokeh Development Team (2014) Bokeh: Python library for interactive visualizationGoogle Scholar
  12. 12.
    Kiss T, Strunk J (2006) Unsupervised multilingual sentence boundary detection. Comput Linguist 32:485–525CrossRefGoogle Scholar
  13. 13.
    Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM 2004Google Scholar
  14. 14.
    Motoda H et al (2013) Advanced data mining and applications. Lect Notes Artif Intell 1:XXII, 588Google Scholar
  15. 15.
    Hutto CJ, Gilbert E (2014) Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: 8th International AAAI Conference on Weblogs and Social MediaGoogle Scholar
  16. 16.
    Keyuan Jiang et al (2016) Construction of a personal experience tweet corpus for health surveillance. ACL 2016 2016:128Google Scholar
  17. 17.
    Louppe G (2014) Accelerating random forests in scikit-learnGoogle Scholar
  18. 18.
    Deng L, Wiebe J (2015) Joint prediction for entity/event-level sentiment analysis using probabilistic soft logic models. In: 2015 Conference on empirical methods in natural language processing. Association for Computational LinguisticsGoogle Scholar
  19. 19.
    Niklas J, Gurevych I (2010) Extracting opinion targets in a single- and cross-domain setting with conditional random fields. In: Conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 1035–1045Google Scholar
  20. 20.
    Zimmermann M et al (2015) Incremental active opinion learning over a stream of opinionated documents. In: WS on issues of sentiment discovery and opinion mining at KDDGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Sourabh Dandage
    • 1
  • Johannes Huber
    • 1
  • Atin Janki
    • 1
  • Uli Niemann
    • 1
  • Ruediger Pryss
    • 2
  • Manfred Reichert
    • 2
  • Steve Harrison
    • 3
  • Markku Vessala
    • 3
  • Winfried Schlee
    • 4
  • Thomas Probst
    • 5
  • Myra Spiliopoulou
    • 1
  1. 1.Otto-von-Guericke UniversityMagdeburgGermany
  2. 2.University of UlmUlmGermany
  3. 3.TinnitusHubEnglandUK
  4. 4.University Hospital RegensburgRegensburgGermany
  5. 5.Donau University KremsKrems an der DonauAustria

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