Adopting graph traversal techniques for context-driven value sets extraction from biomedical knowledge sources
Depressive Disorder, Major
Electronic Health Records
Primary Health Care
The ability to model, share and re-use value sets across multiple medical information systems is an important requirement. However, generating value sets semi-automatically from a terminology service is still an unresolved issue, in part due to the lack of linkage to clinical context patterns that provide the constraints in defining a concept domain and invocation of value sets extraction. Towards this goal, we develop and evaluate an approach for context-driven automatic value sets extraction based on a formal terminology model. The crux of the technique is to identify and define the context patterns from various domains of discourse and leverage them for value set extraction using two complementary ideas based on (i) local terms provided by the subject matter experts (extensional) and (ii) semantic definition of the concepts in coding schemes (intensional). We develop algorithms based on well-studied graph traversal and ontology segmentation techniques for both the approaches and implement a prototype demonstrating their applicability on use cases from, SNOMED CT rendered, in the LexGrid terminology model. We also present preliminary evaluation of our approach and report investigation results done by subject matter experts at the Mayo Clinic.