Corpus-based Approach to Creating a Semantic Lexicon for Clinical Research Eligibility Criteria from UMLS

We describe a corpus-based approach to creating a semantic lexicon using UMLS knowledge sources. We extracted 10,000 sentences from the eligibility criteria sections of clinical trial summaries contained in ClinicalTrials.gov. The UMLS Metathesaurus and SPECIALIST Lexical Tools were used to extract and normalize UMLS recognizable terms. When annotated with Semantic Network types, the corpus had a lexical ambiguity of 1.57 (=total types for unique lexemes / total unique lexemes) and a word occurrence ambiguity of 1.96 (=total type occurrences / total word occurrences). A set of semantic preference rules was developed and applied to completely eliminate ambiguity in semantic type assignment. The lexicon covered 95.95% UMLS-recognizable terms in our corpus. A total of 20 UMLS semantic types, representing about 17% of all the distinct semantic types assigned to corpus lexemes, covered about 80% of the vocabulary of our corpus.