Enhancing clinical concept extraction with distributional semantics.

ABSTRACT: Extracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives constitutes a basic enabling technology to unlock the knowledge within and support more advanced reasoning applications such as diagnosis explanation, disease progression modeling, and intelligent analysis of the effectiveness of treatment. The recent release of annotated training sets of de-identified clinical narratives has contributed to the development and refinement of concept extraction methods. However, as the annotation process is labor-intensive, training data are necessarily limited in the concepts and concept patterns covered, which impacts the performance of supervised machine learning applications trained with these data. This paper proposes an approach to minimize this limitation by combinin

SUBMITTER: Jonnalagadda S 

PROVIDER: S-EPMC3272090 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature