ArticleMethods of information in medicine2011
Effectiveness of lexico-syntactic pattern matching for ontology enrichment with clinical documents.
Article in Methods of information in medicine, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Logical definition-based identification of potential missing concepts in SNOMED CT.BMC medical informatics and decision making · 2023Article
- Lexicon Development for COVID-19-related Concepts Using Open-source Word Embedding Sources: An Intrinsic and Extrinsic Evaluation.JMIR medical informatics · 2021Article
- Workflow Lexicons in Healthcare: Validation of the SWIM Lexicon.Journal of digital imaging · 2017Article
- Towards an Obesity-Cancer Knowledge Base: Biomedical Entity Identification and Relation Detection.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2016Article
- NOBLE - Flexible concept recognition for large-scale biomedical natural language processing.BMC bioinformatics · 2016Article
- Automated concept and relationship extraction for the semi-automated ontology management (SEAM) system.Journal of biomedical semantics · 2015Article
- Measuring diversity in medical reports based on categorized attributes and international classification systems.BMC medical informatics and decision making · 2012Article
- Natural Language Processing methods and systems for biomedical ontology learning.Journal of biomedical informatics · 2011Review
- Similarity-Based Recommendation of New Concepts to a Terminology.AMIA ... Annual Symposium proceedings. AMIA SymposiumArticle
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Authors and funding
6 authors.
Funding
Abstract
objectiveTo evaluate the effectiveness of a lexico-syntactic pattern (LSP) matching method for ontology enrichment using clinical documents.
methodsTwo domains were separately studied using the same methodology. We used radiology documents to enrich RadLex and pathology documents to enrich National Cancer Institute Thesaurus (NCIT). Several known LSPs were used for semantic knowledge extraction. We first retrieved all sentences that contained LSPs across two large clinical repositories, and examined the frequency of the LSPs. From this set, we randomly sampled LSP instances which were examined by human judges. We used a two-step method to determine the utility of these patterns for enrichment. In the first step, domain experts annotated medically meaningful terms (MMTs) from each sentence within the LSP. In the second step, RadLex and NCIT curators evaluated how many of these MMTs could be added to the resource. To quantify the utility of this LSP method, we defined two evaluation metrics: suggestion rate (SR) and acceptance rate (AR). We used these measures to estimate the yield of concepts and relationships, for each of the two domains.
resultsFor NCIT, the concept SR was 24%, and the relationship SR was 65%. The concept AR was 21%, and the relationship AR was 14%. For RadLex, the concept SR was 37%, and the relationship SR was 55%. The concept AR was 11%, and the relationship AR was 44%.
conclusionThe LSP matching method is an effective method for concept and concept relationship discovery in biomedical domains.
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