TMUNSW: Identification of disorders and normalization to SNOMED-CT terminology in unstructured clinical notes

Abstract

Unstructured clinical notes are rich sources for valuable patient information. Information extraction techniques can be employed to extract this valuable information, which in turn can be used to discover new knowledge. Named entity recognition and normalization are the basic tasks involved in information extraction. In this paper, identification of disorder named entities and the mapping of identified disorder entities to SNOMED-CT terminology using UMLS Metathesaurus is presented. A supervised linear chain conditional random field model based on sets of features to predict disorder mentions is used in conjunction with MetaMap to identify and normalize disorders. Error analysis conclude that recall of the developed system can be significantly increased by adding more features during model development and also by using a frame based approach for handling disjoint entities.