title: Applying hybrid algorithms for text matching to automated biomedical vocabulary mapping.
abstract: Several biomedical vocabularies are often used by clinical applications due to their different domain(s) of coverage, intended use, etc. Mapping them to a reference terminology is essential for inter-systems interoperability. Manual vocabulary mapping is labor-intensive and allows room for inconsistencies. It requires manual searching for synonyms, abbreviation expansions, variations, etc., placing additional burden on the mappers. Furthermore, local vocabularies may use non-standard words and abbreviations, posing additional problems. However, much of this process can be automated to provide decision-support, allowing mappers to focus on steps that absolutely need their expertise. We developed hybrid algorithms comprising of rules, permutations, sequence alignment and cost algorithms that utilize the UMLS SPECIALIST Lexicon, a custom knowledgebase and a search engine to automatically find probable matches, allowing mappers to select the best match from this list. We discuss the techniques, results from assisting to map a local codeset, and scope for generalizability.
authors.affiliation: Department of Medical Informatics, University of Utah, Salt Lake City, Utah, USA.
authors.forename: Senthil K
authors.initials: SK
authors.lastname: Nachimuthu
authors.forename: Lee Min
authors.initials: LM
authors.lastname: Lau
mesh_terms: D000465:Algorithms; D016247:Information Storage and Retrieval; D051188:Knowledge Bases; D008491:Medical Informatics Applications; D009323:Natural Language Processing; D009626:Terminology as Topic; D017432:Unified Medical Language System; D018875:Vocabulary, Controlled