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Predicting Medical Roles in Online Health Fora

  • Amine AbdaouiEmail author
  • Jérôme Azé
  • Sandra Bringay
  • Natalia Grabar
  • Pascal Poncelet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8791)

Abstract

Online health fora are increasingly visited by patients to get help and information related to their health. However, these fora are not limited to patients: a significant number of health professionals actively participate in many discussions. As experts their posted information are very important since, they are able to well explain the problems, the symptoms, correct false affirmations and give useful advices, etc. For someone interested in trusty medical information, obtaining only these kinds of posts can be very useful and informative. Unfortunately, extracting such knowledge needs to navigate over the fora in order to evaluate the information. Navigation and selection are time consuming, tedious, difficult and error-prone activities when done manually. It is thus important to propose a new method for automatically categorize information proposed both by non-experts as well as by professionals in online health fora. In this paper, we propose to use a supervised approach to evaluate what are the most representative components of a post considering vocabularies, uncertainty markers, emotions, misspellings and interrogative forms to perform efficiently this categorization. Experiments have been conducted on two real fora and shown that our approach is efficient for extracting posts done by professionals.

Keywords

Text categorization Text mining Online health fora 

Notes

Acknowledgement

This paper is based on studies supported by the “Maison des Sciences de l’Homme de Montpellier” (MSH-M) within the framework of the French project “Patient’s mind”.8

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Amine Abdaoui
    • 1
    Email author
  • Jérôme Azé
    • 1
  • Sandra Bringay
    • 1
  • Natalia Grabar
    • 2
  • Pascal Poncelet
    • 1
  1. 1.LIRMM UM2 CNRS, UMR 5506MontpellierFrance
  2. 2.STL UMR 8163 CNRSUniversité Lille 3Lille 1France

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