Abstract
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.
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Taboada, M., Meizoso, M., Riaño, D., Alonso, A., Martínez, D. (2010). From Natural Language Descriptions in Clinical Guidelines to Relationships in an Ontology. In: Riaño, D., ten Teije, A., Miksch, S., Peleg, M. (eds) Knowledge Representation for Health-Care. Data, Processes and Guidelines. KR4HC 2009. Lecture Notes in Computer Science(), vol 5943. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11808-1_3
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DOI: https://doi.org/10.1007/978-3-642-11808-1_3
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