Exploring Lexical Forms: A First-Generation Consumer Health Vocabulary

1. CHV anorexia hair loss Knee cap D0000023 Heart Attack C0003125 Anorexia nervosa C0027051 C0020678 C0030647 C0002170 Knee bone Hypotrichosis Myocardial infarction alopecia C0003123 hair loss Anorexia UMLS Exploring Lexical Forms: A First-Generation Consumer Health Vocabulary Qing T. Zenga, Tony Tseb, Guy Divitabc, Alla Keselmanbd, Jon Crowella, Allen C. Browneb aDSG, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA bLHNCBC, National Library of Medicine, NIH, DHHS, Bethesda, MD cManagement Systems Designers, Inc., Fairfax, VA dAquilent, Inc., Laurel, MD The Consumer Health Vocabulary Initiative (http://consumerhealthvocab.org/) is a multi-disciplinary effort to facilitate the research and development of consumer health vocabularies (CHVs). The aim of consumer health vocabularies (CHVs) is to help bridge the communication gap, particular between consumers and informatics applications. We have developed a “first-generation” CHV* to address lexical forms used in lay “expressions” (i.e., words or phrases) because surface-level mismatches are more tractable than those involving underlying concept and relations. • The next steps will be: • Improve the coverage of our open access and collaborative (OAC) CHV. • Explore the conceptual and relational levels of CHVs. • Use the OAC CHV to support information retrieval, personal health record, health literacy and etc. applications. • We have focused on identifying three types of forms from a large consumer query log data set and the Unified Medical Language System® (UMLS®) Metathesaurus: • Consumer-friendly display (CFD) forms: focuses on identifying forms that are more familiar to consumers. We have developed automated tools for predicting form difficulty using frequency and contextual information. • Consumer forms with different semantics in professional and lay contexts: many consumer forms are also used by clinicians, but with different semantics. Corpus-based manual review and semi-automated analysis were used to filter such terms for preventing inappropriate mapping. • Consumer forms not in medical vocabularies: after manual review, logistic regression models were trained on the reviewed strings for automated term recognition. *Zeng Q, Tse T. Exploring and developing consumer health vocabularies. J Am Med Inform Assoc. 2006; 3(1):24-29.