# Enriching Consumer Health Vocabulary Using Enhanced GloVe Word Embedding

arXiv: https://arxiv.org/abs/2004.00150 · HTML: https://mlsanity.com/paper/2004.00150/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP](https://mlsanity.com/paper/2312.15561/) (2023)
- [Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions](https://mlsanity.com/paper/2201.00768/) (2022)
- [An Automated Method to Enrich Consumer Health Vocabularies Using GloVe Word Embeddings and An Auxiliary Lexical Resource](https://mlsanity.com/paper/2105.08812/) (2021)
- [WOVe: Incorporating Word Order in GloVe Word Embeddings](https://mlsanity.com/paper/2105.08597/) (2021)

## Abstract (arXiv)

Open-Access and Collaborative Consumer Health Vocabulary (OAC CHV, or CHV for short), is a collection of medical terms written in plain English. It provides a list of simple, easy, and clear terms that laymen prefer to use rather than an equivalent professional medical term. The National Library of Medicine (NLM) has integrated and mapped the CHV terms to their Unified Medical Language System (UMLS). These CHV terms mapped to 56000 professional concepts on the UMLS. We found that about 48% of these laymen's terms are still jargon and matched with the professional terms on the UMLS. In this paper, we present an enhanced word embedding technique that generates new CHV terms from a consumer-generated text. We downloaded our corpus from a healthcare social media and evaluated our new method based on iterative feedback to word embedding using ground truth built from the existing CHV terms. Our feedback algorithm outperformed unmodified GLoVe and new CHV terms have been detected.