Extracting molecular binding relationships from biomedical text
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Journal of Biomedical Informatics - Special issue: Building nursing knowledge through infomatics: from concept representation to data mining
Journal of Biomedical Informatics
An automated system for conversion of clinical notes into SNOMED clinical terminology
ACSW '07 Proceedings of the fifth Australasian symposium on ACSW frontiers - Volume 68
Detecting Intuitive Mentions of Diseases in Narrative Clinical Text
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Two-phase chief complaint mapping to the UMLS metathesaurus in Korean electronic medical records
IEEE Transactions on Information Technology in Biomedicine
Annotating and recognising named entities in clinical notes
ACLstudent '09 Proceedings of the ACL-IJCNLP 2009 Student Research Workshop
Associating Clinical Archetypes Through UMLS Metathesaurus Term Clusters
Journal of Medical Systems
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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.