Augmenting Medical Decision Making With Text-Based Search of Teaching File Repositories and Medical Ontologies

Abstract

Teaching files are widely used by radiologists in the diagnostic process and for student education. Most hospitals maintain an active collection of teaching files for internal purposes, but many teaching files are also publicly available online, some linked to secondary sources. However, public sources offer very limited (and ad-hoc) search capabilities. Based on the previous work on data integration and text-based search, the authors extended their Integrated Radiology Image Search (IRIS 1.1) engine with a new medical ontology, SNOMED CT, and the ICD10 dictionary. IRIS 1.1 integrates public data sources and applies query expansion with exact and partial matches to find relevant teaching files. Using a set of 28 representative queries from multiple sources, the search engine finds more relevant teaching cases versus other publicly available search engines.