Chintan O Patel1, James J Cimino. 1. Department of Biomedical Informatics, Columbia University, Vanderbilt Clinic, 5 Floor, 622 West 168 Street, New York, NY 10032, USA. chintan.patel@dbmi.columbia.edu
Abstract
OBJECTIVE: To use the semantic and structural properties in the Unified Medical Language System (UMLS) Metathesaurus to characterize and discover potential relationships. DESIGN: The UMLS integrates knowledge from several biomedical terminologies. This knowledge can be used to discover implicit semantic relationships between concepts. In this paper, the authors propose a problem-independent approach for discovering potential terminological relationships that employs semantic abstraction of indirect relationship paths to perform classification and analysis of network theoretical measures such as topological overlap, preferential attachment, graph partitioning, and number of indirect paths. Using different versions of the UMLS, the authors evaluate the proposed approach's ability to predict newly added relationships. MEASUREMENTS: Classification accuracy, precision-recall. RESULTS: Strong discriminative characteristics were observed with a semantic abstraction based classifier (classification accuracy of 91%), the average number of indirect paths, preferential attachment, and graph partitioning to identify potential relationships. The proposed relationship prediction algorithm resulted in 56% recall in top 10 results for new relationships added to subsequent versions of the UMLS between 2005 and 2007. CONCLUSIONS: The UMLS has sufficient knowledge to enable discovery of potential terminological relationships.
OBJECTIVE: To use the semantic and structural properties in the Unified Medical Language System (UMLS) Metathesaurus to characterize and discover potential relationships. DESIGN: The UMLS integrates knowledge from several biomedical terminologies. This knowledge can be used to discover implicit semantic relationships between concepts. In this paper, the authors propose a problem-independent approach for discovering potential terminological relationships that employs semantic abstraction of indirect relationship paths to perform classification and analysis of network theoretical measures such as topological overlap, preferential attachment, graph partitioning, and number of indirect paths. Using different versions of the UMLS, the authors evaluate the proposed approach's ability to predict newly added relationships. MEASUREMENTS: Classification accuracy, precision-recall. RESULTS: Strong discriminative characteristics were observed with a semantic abstraction based classifier (classification accuracy of 91%), the average number of indirect paths, preferential attachment, and graph partitioning to identify potential relationships. The proposed relationship prediction algorithm resulted in 56% recall in top 10 results for new relationships added to subsequent versions of the UMLS between 2005 and 2007. CONCLUSIONS: The UMLS has sufficient knowledge to enable discovery of potential terminological relationships.
Authors: Kevin Bretonnel Cohen; Benjamin Glass; Hansel M Greiner; Katherine Holland-Bouley; Shannon Standridge; Ravindra Arya; Robert Faist; Diego Morita; Francesco Mangano; Brian Connolly; Tracy Glauser; John Pestian Journal: Biomed Inform Insights Date: 2016-05-22