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An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource
BACKGROUND: Clear language makes communication easier between any two parties. A layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical terminology which can...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371999/ https://www.ncbi.nlm.nih.gov/pubmed/34458573 http://dx.doi.org/10.7717/peerj-cs.668 |
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author | Ibrahim, Mohammed Gauch, Susan Salman, Omar Alqahtani, Mohammed |
author_facet | Ibrahim, Mohammed Gauch, Susan Salman, Omar Alqahtani, Mohammed |
author_sort | Ibrahim, Mohammed |
collection | PubMed |
description | BACKGROUND: Clear language makes communication easier between any two parties. A layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical terminology which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. OBJECTIVE: Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this paper, we present an automatic method to enrich laymen’s vocabularies that has the benefit of being able to be applied to vocabularies in any domain. METHODS: Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. Our approach further improves the consumer health vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. The basic GloVe and our novel algorithms incorporating WordNet were evaluated using two laymen datasets from the National Library of Medicine (NLM), Open-Access Consumer Health Vocabulary (OAC CHV) and MedlinePlus Healthcare Vocabulary. RESULTS: The results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Furthermore, our enhanced GloVe approach outperformed basic GloVe with an average F-score of 61%, a relative improvement of 25%. Furthermore, the enhanced GloVe showed a statistical significance over the two ground truth datasets with P < 0.001. CONCLUSIONS: This paper presents an automatic approach to enrich consumer health vocabularies using the GloVe word embeddings and an auxiliary lexical source, WordNet. Our approach was evaluated used healthcare text downloaded from MedHelp.org, a healthcare social media platform using two standard laymen vocabularies, OAC CHV, and MedlinePlus. We used the WordNet ontology to expand the healthcare corpus by including synonyms, hyponyms, and hypernyms for each layman term occurrence in the corpus. Given a seed term selected from a concept in the ontology, we measured our algorithms’ ability to automatically extract synonyms for those terms that appeared in the ground truth concept. We found that enhanced GloVe outperformed GloVe with a relative improvement of 25% in the F-score. |
format | Online Article Text |
id | pubmed-8371999 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83719992021-08-26 An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource Ibrahim, Mohammed Gauch, Susan Salman, Omar Alqahtani, Mohammed PeerJ Comput Sci Bioinformatics BACKGROUND: Clear language makes communication easier between any two parties. A layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical terminology which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. OBJECTIVE: Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this paper, we present an automatic method to enrich laymen’s vocabularies that has the benefit of being able to be applied to vocabularies in any domain. METHODS: Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. Our approach further improves the consumer health vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. The basic GloVe and our novel algorithms incorporating WordNet were evaluated using two laymen datasets from the National Library of Medicine (NLM), Open-Access Consumer Health Vocabulary (OAC CHV) and MedlinePlus Healthcare Vocabulary. RESULTS: The results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Furthermore, our enhanced GloVe approach outperformed basic GloVe with an average F-score of 61%, a relative improvement of 25%. Furthermore, the enhanced GloVe showed a statistical significance over the two ground truth datasets with P < 0.001. CONCLUSIONS: This paper presents an automatic approach to enrich consumer health vocabularies using the GloVe word embeddings and an auxiliary lexical source, WordNet. Our approach was evaluated used healthcare text downloaded from MedHelp.org, a healthcare social media platform using two standard laymen vocabularies, OAC CHV, and MedlinePlus. We used the WordNet ontology to expand the healthcare corpus by including synonyms, hyponyms, and hypernyms for each layman term occurrence in the corpus. Given a seed term selected from a concept in the ontology, we measured our algorithms’ ability to automatically extract synonyms for those terms that appeared in the ground truth concept. We found that enhanced GloVe outperformed GloVe with a relative improvement of 25% in the F-score. PeerJ Inc. 2021-08-09 /pmc/articles/PMC8371999/ /pubmed/34458573 http://dx.doi.org/10.7717/peerj-cs.668 Text en © 2021 Ibrahim et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Ibrahim, Mohammed Gauch, Susan Salman, Omar Alqahtani, Mohammed An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource |
title | An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource |
title_full | An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource |
title_fullStr | An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource |
title_full_unstemmed | An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource |
title_short | An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource |
title_sort | automated method to enrich consumer health vocabularies using glove word embeddings and an auxiliary lexical resource |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371999/ https://www.ncbi.nlm.nih.gov/pubmed/34458573 http://dx.doi.org/10.7717/peerj-cs.668 |
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