Towards linking patients and clinical information: detecting UMLS concepts in e-mail

The purpose of this project is to explore the feasibility of detecting terms within the electronic messages of patients that could be used to effectively search electronic knowledge resources and bring health information resources into the hands of patients. Our team is exploring the application of the natural language processing (NLP) tools built within the Lister Hill Center at the National Library of Medicine (NLM) to the challenge of detecting relevant concepts from the Unified Medical Language System (UMLS) within the free text of lay people's electronic messages (e-mail). We obtained a sample of electronic messages sent by patients participating in a randomized field evaluation of an internet-based home care support service to the project nurse, and we subjected elements of these messages to a series of analyses using several vocabularies from the UMLS Metathesaurus and the selected NLP tools. The nursing vocabularies provide an excellent starting point for this exercise because their domain encompasses patient's responses to health challenges. In successive runs we augmented six nursing vocabularies (NANDA Nursing Diagnosis, Nursing Interventions Classification, Nursing Outcomes Classification, Home Health Classification, Omaha System, and the Patient Care Data Set) with selected sets of clinical terminologies (International Classification of Primary Care; International Classification of Primary Care- American English; Micromedex DRUGDEX; National Drug Data File; Thesaurus of Psychological Terms; WHO Adverse Drug Reaction Terminology) and then additionally with either Medical Subject Heading (MeSH) or SNOMED International terms. The best performance was obtained when the nursing vocabularies were complemented with selected clinical terminologies. These findings have implications not only for facilitating lay people's access to electronic knowledge resources but may also be of assistance in developing new tools to aid in linking free text (e.g., clinical notes) to lexically complex knowledge resources such as those emerging from the Human Genome Project.