  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.
