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Background: Clear language makes communication easier between any twoparties. A layman may have difficulty communicating with a professional due tonot understanding the specialized terms common to the domain. In healthcare, itis rare to find a layman knowledgeable in medical terminology which can lead topoor understanding of their condition and/or treatment. To bridge this gap,several professional vocabularies and ontologies have been created to maplaymen medical terms to professional medical terms and vice versa.  Objective: Many of the presented vocabularies are built manually orsemi-automatically requiring large investments of time and human effort andconsequently the slow growth of these vocabularies. In this paper, we presentan automatic method to enrich laymen's vocabularies that has the benefit ofbeing able to be applied to vocabularies in any domain.  Methods: Our entirely automatic approach uses machine learning, specificallyGlobal Vectors for Word Embeddings (GloVe), on a corpus collected from a socialmedia healthcare platform to extend and enhance consumer health vocabularies(CHV). Our approach further improves the CHV by incorporating synonyms andhyponyms from the WordNet ontology. The basic GloVe and our novel algorithmsincorporating WordNet were evaluated using two laymen datasets from theNational Library of Medicine (NLM), Open-Access Consumer Health Vocabulary (OACCHV) and MedlinePlus Healthcare Vocabulary.  Results: The results show that GloVe was able to find new laymen terms withan F-score of 48.44%. Furthermore, our enhanced GloVe approach outperformedbasic GloVe with an average F-score of 61%, a relative improvement of 25%.Furthermore, the enhanced GloVe showed a statistical significance over the twoground truth datasets with P<.001.

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