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Collecting specialty-related medical terms: Development and evaluation of a resource for Spanish

Authors
  • López-Úbeda, Pilar1
  • Pomares-Quimbaya, Alexandra2
  • Díaz-Galiano, Manuel Carlos1
  • Schulz, Stefan3
  • 1 Universidad de Jaén, Campus Las Lagunillas, s/n, Jaén, 23071, Spain , Jaén (Spain)
  • 2 Pontificia Universidad Javeriana, Cra. 7 No 40-62, Bogotá, 110231, Colombia , Bogotá (Colombia)
  • 3 Medical University of Graz, Auenbruggerpl No 2, Graz, 8036, Austria , Graz (Austria)
Type
Published Article
Journal
BMC Medical Informatics and Decision Making
Publisher
Springer (Biomed Central Ltd.)
Publication Date
May 04, 2021
Volume
21
Issue
1
Identifiers
DOI: 10.1186/s12911-021-01495-w
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundControlled vocabularies are fundamental resources for information extraction from clinical texts using natural language processing (NLP). Standard language resources available in the healthcare domain such as the UMLS metathesaurus or SNOMED CT are widely used for this purpose, but with limitations such as lexical ambiguity of clinical terms. However, most of them are unambiguous within text limited to a given clinical specialty. This is one rationale besides others to classify clinical text by the clinical specialty to which they belong.ResultsThis paper addresses this limitation by proposing and applying a method that automatically extracts Spanish medical terms classified and weighted per sub-domain, using Spanish MEDLINE titles and abstracts as input. The hypothesis is biomedical NLP tasks benefit from collections of domain terms that are specific to clinical subdomains. We use PubMed queries that generate sub-domain specific corpora from Spanish titles and abstracts, from which token n-grams are collected and metrics of relevance, discriminatory power, and broadness per sub-domain are computed. The generated term set, called Spanish core vocabulary about clinical specialties (SCOVACLIS), was made available to the scientific community and used in a text classification problem obtaining improvements of 6 percentage points in the F-measure compared to the baseline using Multilayer Perceptron, thus demonstrating the hypothesis that a specialized term set improves NLP tasks.ConclusionThe creation and validation of SCOVACLIS support the hypothesis that specific term sets reduce the level of ambiguity when compared to a specialty-independent and broad-scope vocabulary.

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