Rapid alignment of text to the UMLS and integration into a VIVO repository Stony Brook Medicine Janos G. Hajagos Acknowledgements: The Stony Brook Medicine SUNY Reach team created the infrastructure and generated the data used in this analysis. Members of the team include: Erich Bremer, Barbara Chandler, Moises Eisenberg, Jizu Zhi, Tammy DiPrima, and Andrew White. A prototype disproportion- ate analysis tool was developed by Alex Schultz. This is an extension of work supported through CTSAconnect. CTSA 10-001: 100928SB23 Uniﬁed Medical Language System (UMLS) The goal of this research is to extract SUIs (String Unique Identiﬁers) from text associated with a faculty member‘s VIVO proﬁle. SUIs unlike CUIs (Concept Unique Identiﬁers) do not have a conceptual meaning. A single SUI can be linked to several concepts. As an example, below the SUI (S0004723) is linked to two diﬀerent CUIs (C0580209 & C1302752). It is diﬃcult for any automated system to determine with 100% accuracy the contextual meaning of a word or a phrase. The approach taken here is to utilize the sta- tistical patterns in the data to automate the process of tagging large quantities of text. With SUIs there is still a path on the graph to a conceptual meaning. . An extract from MRCONSO showing the relationships between SUIs and CUIs The SUNY REACH site <http://reach.suny.edu/> and the data set Generating RDF for import into VIVO The VIVO 1.5 ontology around research area Identifying what fragments make a researcher unique For the analysis the UMLS release 2012ab was utilized. Stony Brook maintains a SPARQL endpoint: http://link.informatics.stonybro ok.edu/sparql/ A short text alignment service: http://link.informatics.stonybro ok.edu/MeaningLookup/ To align MeSH CUIs and get abstracts and titles from articles two web services (2013 XML): http://link.informatics.stonybro ok.edu/weaver/pubmed2cuis?p mid=21325273 and http://link.informatics.stonybro ok.edu/weaver/pubmed2abstra ct?pmid=10343245 Filter fragments through a UMLS source vocabulary Sample alignment results NCIt is a source vocabulary Breaking text into fragments Natural language processing, abbreviated as NLP, is an active area of research and devel- opment. NLTK is a well supported NLP toolkit for the Python language. There is even a free book: <http://nltk.org/book/>. The following features were utilized: parsing paragraphs into sentences, parsing sen- tences into words, and removing common stop words. Phrases were matched up to the length of fours words. Only a small subset of the features of the NLTK were used in this research. Utilizing NLTK in the iPython shell How well did the raw alignment method work? Excerpt from the VIVO faculty member’s html page: “During the last several years, he has focused his eﬀorts on the prevention of colon cancer using traditional NSAIDs, NO- donating NSAIDs and other pharmacologi- cal agents. He has also pioneered the appli- cation of infrared spectroscopy to biology with emphasis on cancer holding several relevant patents. A new area of work con- cerns the use of nanotechnology in the pre- vention of cancer.” Terms extracted from the article abstract Terms extracted from article titles MeSH from Medline expert tagged articles Comparison for a single VIVO faculty member Number of articles with abs tracts Number of faculty Number of matched fragments Number of s entences 48,893 621,686 521 5,686 Number of articles Number of faculty Number of matched fragments 216,842 683 22,372 Number of MeS H Number of articles Number of faculty 706 21,954 351,273 Counts on analyzed abstracts Counts on analyzed article titles Counts on Medline MeSH Overall top phrases extracted from article abstract Disproportionate or Market Basket Analysis 1. Compute all possible combination of fragments within a sentence that do not overlap 2. Aggregrate the co-occurrence of the two fragments (pair count) 3. Count the number of times each fragment occurs overall (total fragment count) 4. Calculate the