Incorporating syntactic dependency information towards improved coding of lengthy medical concepts in clinical reports

Abstract

Medical concepts in clinical reports can be found with a high degree of variability of expression. Normalizing medical concepts to standardized vocabularies is a common way of accounting for this variability. One of the challenges in medical concept normalization is the difficulty in comparing two concepts which are orthographically different in representation but are identical in meaning. In this work we describe a method to compare medical phrases by utilizing the information found in syntactic dependencies. We collected a large corpus of radiology reports from our university medical center. A shallow semantic parser was used to identify anatomical phrases. We performed a series of transformations to convert the anatomical phrase into a normalized syntactic dependency representation. The new representation provides an easy intuitive way of comparing the phrases for the purpose of concept normalization.

Extracting Communities of Interests for Semantics-Based Graph Searches

“…We define instances as content items that a user is interested in and classes are defined by the taxonomy. This definition of semantic tag follows those made in the previous research studies [2], [3], [5].…”