Knowledge Discovery on Incompatibility of Medical Concepts

  • Adam Grycner
  • Patrick Ernst
  • Amy Siu
  • Gerhard Weikum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7816)


This work proposes a method for automatically discovering incompatible medical concepts in text corpora. The approach is distantly supervised based on a seed set of incompatible concept pairs like symptoms or conditions that rule each other out. Two concepts are considered incompatible if their definitions match a template, and contain an antonym pair derived from WordNet, VerbOcean, or a hand-crafted lexicon. Our method creates templates from dependency parse trees of definitional texts, using seed pairs. The templates are applied to a text corpus, and the resulting candidate pairs are categorized and ranked by statistical measures. Since experiments show that the results face semantic ambiguity problems, we further cluster the results into different categories. We applied this approach to the concepts in Unified Medical Language System, Human Phenotype Ontology, and Mammalian Phenotype Ontology. Out of 77,496 definitions, 1,958 concept pairs were detected as incompatible with an average precision of 0.80.


Biomedical Domain Medical Concept Concept Pair Embryonal Rhabdomyosarcoma Phenotype Ontology 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Adam Grycner
    • 1
  • Patrick Ernst
    • 1
  • Amy Siu
    • 1
  • Gerhard Weikum
    • 1
  1. 1.Max-Planck Institute for InformaticsSaarbrückenGermany

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