ArticleJournal of biomedical semantics2010
Rewriting and suppressing UMLS terms for improved biomedical term identification.
Article in Journal of biomedical semantics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- Gene Ontology synonym generation rules lead to increased performance in biomedical concept recognition.Journal of biomedical semantics · 2016Article
- Common disease signatures from gene expression analysis in Huntington's disease human blood and brain.Orphanet journal of rare diseases · 2016Article
- An Informatics Approach to Evaluating Combined Chemical Exposures from Consumer Products: A Case Study of Asthma-Associated Chemicals and Potential Endocrine Disruptors.Environmental health perspectives · 2016Article
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- Identifying named entities from PubMed for enriching semantic categories.BMC bioinformatics · 2015Article
- JuFiT: A Configurable Rule Engine for Filtering and Generating New Multilingual Umls Terms.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
- Quantifying the impact and extent of undocumented biomedical synonymy.PLoS computational biology · 2014Article
- Knowledge-based extraction of adverse drug events from biomedical text.BMC bioinformatics · 2014Article
- Molecularly and clinically related drugs and diseases are enriched in phenotypically similar drug-disease pairs.Genome medicine · 2014Article
- Unified Medical Language System term occurrences in clinical notes: a large-scale corpus analysis.Journal of the American Medical Informatics Association : JAMIA · 2012Article
- Towards a semantic lexicon for clinical natural language processing.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012Article
- Using electronic patient records to discover disease correlations and stratify patient cohorts.PLoS computational biology · 2011Article
- Using rule-based natural language processing to improve disease normalization in biomedical text.Journal of the American Medical Informatics Association : JAMIAArticle
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Authors and funding
5 authors.
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Abstract
backgroundIdentification of terms is essential for biomedical text mining.. We concentrate here on the use of vocabularies for term identification, specifically the Unified Medical Language System (UMLS). To make the UMLS more suitable for biomedical text mining we implemented and evaluated nine term rewrite and eight term suppression rules. The rules rely on UMLS properties that have been identified in previous work by others, together with an additional set of new properties discovered by our group during our work with the UMLS. Our work complements the earlier work in that we measure the impact on the number of terms identified by the different rules on a MEDLINE corpus. The number of uniquely identified terms and their frequency in MEDLINE were computed before and after applying the rules. The 50 most frequently found terms together with a sample of 100 randomly selected terms were evaluated for every rule.
resultsFive of the nine rewrite rules were found to generate additional synonyms and spelling variants that correctly corresponded to the meaning of the original terms and seven out of the eight suppression rules were found to suppress only undesired terms. Using the five rewrite rules that passed our evaluation, we were able to identify 1,117,772 new occurrences of 14,784 rewritten terms in MEDLINE. Without the rewriting, we recognized 651,268 terms belonging to 397,414 concepts; with rewriting, we recognized 666,053 terms belonging to 410,823 concepts, which is an increase of 2.8% in the number of terms and an increase of 3.4% in the number of concepts recognized. Using the seven suppression rules, a total of 257,118 undesired terms were suppressed in the UMLS, notably decreasing its size. 7,397 terms were suppressed in the corpus.
conclusionsWe recommend applying the five rewrite rules and seven suppression rules that passed our evaluation when the UMLS is to be used for biomedical term identification in MEDLINE. A software tool to apply these rules to the UMLS is freely available at http://biosemantics.org/casper.
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