Guoqian Jiang1, Christopher G Chute. 1. Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. jiang.guoqian@mayo.edu
Abstract
OBJECTIVE: This study sought to develop and evaluate an approach for auditing the semantic completeness of the SNOMED CT contents using a formal concept analysis (FCA)-based model. DESIGN: We developed a model for formalizing the normal forms of SNOMED CT expressions using FCA. Anonymous nodes, identified through the analyses, were retrieved from the model for evaluation. Two quasi-Poisson regression models were developed to test whether anonymous nodes can evaluate the semantic completeness of SNOMED CT contents (Model 1), and for testing whether such completeness differs between 2 clinical domains (Model 2). The data were randomly sampled from all the contexts that could be formed in the 2 largest domains: Procedure and Clinical Finding. Case studies (n = 4) were performed on randomly selected anonymous node samples for validation. MEASUREMENTS: In Model 1, the outcome variable is the number of fully defined concepts within a context, while the explanatory variables are the number of lattice nodes and the number of anonymous nodes. In Model 2, the outcome variable is the number of anonymous nodes and the explanatory variables are the number of lattice nodes and a binary category for domain (Procedure/Clinical Finding). RESULTS: A total of 5,450 contexts from the 2 domains were collected for analyses. Our findings revealed that the number of anonymous nodes had a significant negative correlation with the number of fully defined concepts within a context (p < 0.001). Further, the Clinical Finding domain had fewer anonymous nodes than the Procedure domain (p < 0.001). Case studies demonstrated that the anonymous nodes are an effective index for auditing SNOMED CT. CONCLUSION: The anonymous nodes retrieved from FCA-based analyses are a candidate proxy for the semantic completeness of the SNOMED CT contents. Our novel FCA-based approach can be useful for auditing the semantic completeness of SNOMED CT contents, or any large ontology, within or across domains.
OBJECTIVE: This study sought to develop and evaluate an approach for auditing the semantic completeness of the SNOMED CT contents using a formal concept analysis (FCA)-based model. DESIGN: We developed a model for formalizing the normal forms of SNOMED CT expressions using FCA. Anonymous nodes, identified through the analyses, were retrieved from the model for evaluation. Two quasi-Poisson regression models were developed to test whether anonymous nodes can evaluate the semantic completeness of SNOMED CT contents (Model 1), and for testing whether such completeness differs between 2 clinical domains (Model 2). The data were randomly sampled from all the contexts that could be formed in the 2 largest domains: Procedure and Clinical Finding. Case studies (n = 4) were performed on randomly selected anonymous node samples for validation. MEASUREMENTS: In Model 1, the outcome variable is the number of fully defined concepts within a context, while the explanatory variables are the number of lattice nodes and the number of anonymous nodes. In Model 2, the outcome variable is the number of anonymous nodes and the explanatory variables are the number of lattice nodes and a binary category for domain (Procedure/Clinical Finding). RESULTS: A total of 5,450 contexts from the 2 domains were collected for analyses. Our findings revealed that the number of anonymous nodes had a significant negative correlation with the number of fully defined concepts within a context (p < 0.001). Further, the Clinical Finding domain had fewer anonymous nodes than the Procedure domain (p < 0.001). Case studies demonstrated that the anonymous nodes are an effective index for auditing SNOMED CT. CONCLUSION: The anonymous nodes retrieved from FCA-based analyses are a candidate proxy for the semantic completeness of the SNOMED CT contents. Our novel FCA-based approach can be useful for auditing the semantic completeness of SNOMED CT contents, or any large ontology, within or across domains.
Authors: S Trent Rosenbloom; Randolph A Miller; Kevin B Johnson; Peter L Elkin; Steven H Brown Journal: J Am Med Inform Assoc Date: 2006-02-24 Impact factor: 4.497
Authors: Peter L Elkin; Steven H Brown; Casey S Husser; Brent A Bauer; Dietlind Wahner-Roedler; S Trent Rosenbloom; Ted Speroff Journal: Mayo Clin Proc Date: 2006-06 Impact factor: 7.616
Authors: Christopher Ochs; James Geller; Yehoshua Perl; Yan Chen; Junchuan Xu; Hua Min; James T Case; Zhi Wei Journal: J Am Med Inform Assoc Date: 2014-10-21 Impact factor: 4.497