Mining cross-terminology links in the UMLS.
OBJECTIVE
To explore link mining approaches over transitive relationship paths in the Unified Medical Language System (UMLS). The goal is to classify relevant and 'interesting' cross-terminology links/paths for integration of Electronic Health Records (EHRs) and information resources.


METHODS
We present approaches for using the link semantics as learning features, sampling the UMLS to create training examples, and ranking the classified links. We use the clinical query and MEDLINE pairs in the OHSUMED dataset to extract 'gold-links' between SNOMED-CT and MeSH respectively, and compare them against corresponding two-step transitive links generated from the UMLS.


RESULTS
a). 75.7% increase in reachable MeSH concepts with two-step links as compared to direct one-step links b). 94.08% recall after link classification.


CONCLUSIONS
Using link mining with the UMLS is a promising approach for inter-terminology translation; further research is needed to handle the exponential link growth.
Patel
Chintan O
CO
Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Cimino
James J
JJ
D016247:Information Storage and Retrieval; D016239:MEDLINE; D016347:Medical Records Systems, Computerized; D046650:Medical Subject Headings; D009323:Natural Language Processing; D039061:Systematized Nomenclature of Medicine; D018511:Systems Integration; D017432:Unified Medical Language System