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Weakly-Supervised Symptom Recognition for Rare Diseases in Biomedical Text

  • Pierre HolatEmail author
  • Nadi Tomeh
  • Thierry Charnois
  • Delphine Battistelli
  • Marie-Christine Jaulent
  • Jean-Philippe Métivier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9897)

Abstract

In this paper, we tackle the issue of symptom recognition for rare diseases in biomedical texts. Symptoms typically have more complex and ambiguous structure than other biomedical named entities. Furthermore, existing resources are scarce and incomplete. Therefore, we propose a weakly-supervised framework based on a combination of two approaches: sequential pattern mining under constraints and sequence labeling. We use unannotated biomedical paper abstracts with dictionaries of rare diseases and symptoms to create our training data. Our experiments show that both approaches outperform simple projection of the dictionaries on text, and their combination is beneficial. We also introduce a novel pattern mining constraint based on semantic similarity between words inside patterns.

Keywords

Information extraction Pattern mining CRF Symptoms recognition Biomedical texts 

Notes

Acknowledgments

This work is supported by the French National Research Agency (ANR) as part of the project Hybride ANR-11-BS02-002 and the “Investissements d’Avenir” program (reference: ANR-10-LABX-0083).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Pierre Holat
    • 1
    Email author
  • Nadi Tomeh
    • 1
  • Thierry Charnois
    • 1
  • Delphine Battistelli
    • 2
  • Marie-Christine Jaulent
    • 3
  • Jean-Philippe Métivier
    • 4
  1. 1.LIPNUniversity of Paris 13, Sorbonne Paris CitéParisFrance
  2. 2.MoDyCoUniversity of Paris Ouest Nanterre La DéfenseParisFrance
  3. 3.InsermParisFrance
  4. 4.GREYCUniversity of Caen Basse-NormandieCaenFrance

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