Automating SNOMED coding using medical language understanding: a feasibility study.

This paper evaluates qualitatively the use of the MedLEE natural language processing system to code medical narratives directly into the SNOMED nomenclature, while retaining the MedLEE information model data structure. A gold standard is produced from narrative text manually coded in SNOMED. An automated parsing and SNOMED-coding of the narrative text is then automatically generated by MedLEE. By comparing MedLEE s output to that of the Gold Standard, the capacities of SNOMED and MedLEE to represent the clinical information are subsequently evaluated leading to qualitative observations on their respective strengths and constraints. In this study, MedLEE did code to SNOMED and captures the codes in a sub-structure amenable to interoperability with the description logic of SNOMED RT, showing an approach that augments and formalizes SNOMED s compositional representation methods to accurately capture information from clinical narratives.