# An Automated Method to Enrich Consumer Health Vocabularies Using GloVe Word Embeddings and An Auxiliary Lexical Resource

arXiv: https://arxiv.org/abs/2105.08812 · HTML: https://mlsanity.com/paper/2105.08812/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 109 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Medical Text Classification using Convolutional Neural Networks](https://mlsanity.com/paper/1704.06841/) (2017)
- [Global Vectors for Node Representations](https://mlsanity.com/paper/1902.11004/) (2019)
- [Sequence Learning with RNNs for Medical Concept Normalization in User-Generated Texts](https://mlsanity.com/paper/1811.11523/) (2018)
- [Enriching Consumer Health Vocabulary Using Enhanced GloVe Word Embedding](https://mlsanity.com/paper/2004.00150/) (2020)
- [Medical Synonym Extraction with Concept Space Models](https://mlsanity.com/paper/1506.00528/) (2015)
- [Applying deep learning techniques on medical corpora from the World Wide Web: a prototypical system and evaluation](https://mlsanity.com/paper/1502.03682/) (2015)

## Built on by (most cited first, 1 of 1)

- [Constructing Cross-lingual Consumer Health Vocabulary with Word-Embedding from Comparable User Generated Content](https://mlsanity.com/paper/2206.11612/) (2022)

## Abstract (arXiv)

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 (CHV). Our approach further improves the CHV 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<.001.