From Natural Language Descriptions in Clinical Guidelines to Relationships in an Ontology

  • María Taboada
  • María Meizoso
  • David Riaño
  • Albert Alonso
  • Diego Martínez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5943)


Knowledge Engineering allows to automate entity recognition and relation extraction from clinical texts, which in turn can be used to facilitate clinical practice guideline (CPG) modeling. This paper presents a method to recognize diagnosis and therapy entities, and to identify relationships between these entities from CPG free-text documents. Our approach applies a sequential combination of several basic methods classically used in knowledge engineering (natural language processing techniques, manually authored grammars, lexicons and ontologies), to gradually map sentences describing diagnostic and therapeutic procedures to an ontology. First, using a standardized vocabulary, our method automatically identifies guideline concepts. Next, for each sentence, it determines the patient conditions under which the descriptive knowledge of the sentence is valid. Then, it detects the central information units in the sentence, in order to match the sentence with a small set of predefined relationships. The approach enables automated extraction of relationships about findings that have manifestation in a disease, and procedures that diagnose or treat a disease.


knowledge engineering ontologies UMLS clinical practice guidelines 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • María Taboada
    • 1
  • María Meizoso
    • 1
  • David Riaño
    • 2
  • Albert Alonso
    • 3
  • Diego Martínez
    • 4
  1. 1.Department of Electronics and Computer ScienceUniversity of Santiago de CompostelaSpain
  2. 2.Research Group on AIRovira i Virgili UniversityTarragonaSpain
  3. 3.Hospital ClinicBarcelonaSpain
  4. 4.Department of Applied PhysicsUniversity of Santiago de CompostelaSpain

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