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Literature DB >> 21347110

A Comprehensive Analysis of Five Million UMLS Metathesaurus Terms Using Eighteen Million MEDLINE Citations.

Abstract

The Unified Medical Language System (UMLS) Metathesaurus is widely used for biomedical natural language processing (NLP) tasks. In this study, we systematically analyzed UMLS Metathesaurus terms by analyzing their occurrences in over 18 million MEDLINE abstracts. Our goals were: 1. analyze the frequency and syntactic distribution of Metathesaurus terms in MEDLINE; 2. create a filtered UMLS Metathesaurus based on the MEDLINE analysis; 3. augment the UMLS Metathesaurus where each term is associated with metadata on its MEDLINE frequency and syntactic distribution statistics. After MEDLINE frequency-based filtering, the augmented UMLS Metathesaurus contains 518,835 terms and is roughly 13% of its original size. We have shown that the syntactic and frequency information is useful to identify errors in the Metathesaurus. This filtered and augmented UMLS Metathesaurus can potentially be used to improve efficiency and precision of UMLS-based information retrieval and NLP tasks.

Mesh：

Year: 2010 PMID： 21347110 PMCID： PMC3041393

Source DB: PubMed Journal: AMIA Annu Symp Proc ISSN： 1559-4076

12 in total

1. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program.

Authors: A R Aronson
Journal: Proc AMIA Symp Date: 2001

2. Finding UMLS Metathesaurus concepts in MEDLINE.

Authors: Suresh Srinivasan; Thomas C Rindflesch; William T Hole; Alan R Aronson; James G Mork
Journal: Proc AMIA Symp Date: 2002

3. The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text.

Authors: Thomas C Rindflesch; Marcelo Fiszman
Journal: J Biomed Inform Date: 2003-12 Impact factor: 6.317

A Comprehensive Analysis of Five Million UMLS Metathesaurus Terms Using Eighteen Million MEDLINE Citations.

1. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program.

2. Finding UMLS Metathesaurus concepts in MEDLINE.

3. The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text.

4. A study of biomedical concept identification: MetaMap vs. people.

5. Unsupervised method for extracting machine understandable medical knowledge from a large free text collection.

6. Analysis of a study of the users, uses, and future agenda of the UMLS.

7. Unsupervised method for automatic construction of a disease dictionary from a large free text collection.

8. The Unified Medical Language System.

9. The open biomedical annotator.

10. Comparison of concept recognizers for building the Open Biomedical Annotator.

1. Usability-driven pruning of large ontologies: the case of SNOMED CT.

2. Using ontology-based annotation to profile disease research.

3. Text mining for adverse drug events: the promise, challenges, and state of the art.

4. Neophilia Ranking of Scientific Journals.

Review 5. Recent progress in automatically extracting information from the pharmacogenomic literature.

6. The Lexicon Builder Web service: Building Custom Lexicons from two hundred Biomedical Ontologies.

7. Molecularly and clinically related drugs and diseases are enriched in phenotypically similar drug-disease pairs.

8. Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records.

9. Mining clinical text for signals of adverse drug-drug interactions.

10. Cross-domain targeted ontology subsets for annotation: the case of SNOMED CORE and RxNorm.