Methods for automated concept mapping between medical databases.
The retrieval and exchange of information between medical databases is often impeded by the semantic heterogeneity of concepts contained within the databases. Manual identification of equivalent database elements consumes time and resources, and may often be the rate-limiting technological step in integrating disparate data sources. By employing semantic networks as an intermediary representation of the native databases, automated mapping algorithms can identify equivalent concepts in disparate databases. The algorithms take advantage of the conceptual "context" embodied within a semantic network to produce candidate concept mappings. The performance of automated concept mapping was evaluated by creating semantic network representations for two test laboratory databases. The mapping algorithms identified all equivalent concepts that were present in the databases, and did not leave any equivalent concepts unmapped. The utilization of conceptual context to perform automated concept mapping facilitates the identification of equivalent database concepts and may help decrease the work and costs associated with retrieval and integration of information from disparate databases.
Sun
Yao
Y
Newborn Medicine Informatics Program Children's Hospital, Boston, MA, USA. yao.sun@childrens.harvard.edu
D000465:Algorithms; D001185:Artificial Intelligence; D003628:Database Management Systems; D016208:Databases, Factual; D004282:Documentation / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods*; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D018511:Systems Integration; D017432:Unified Medical Language System; D014584:User-Computer Interface