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BACKGROUND: The amount of biomedical literature is rapidly growing and it isbecoming increasingly difficult to keep manually curated knowledge bases andontologies up-to-date. In this study we applied the word2vec deep learningtoolkit to medical corpora to test its potential for identifying relationshipsfrom unstructured text. We evaluated the efficiency of word2vec in identifyingproperties of pharmaceuticals based on mid-sized, unstructured medical textcorpora available on the web. Properties included relationships to diseases('may treat') or physiological processes ('has physiological effect'). Wecompared the relationships identified by word2vec with manually curatedinformation from the National Drug File - Reference Terminology (NDF-RT)ontology as a gold standard. RESULTS: Our results revealed a maximum accuracyof 49.28% which suggests a limited ability of word2vec to capture linguisticregularities on the collected medical corpora compared with other publishedresults. We were able to document the influence of different parameter settingson result accuracy and found and unexpected trade-off between ranking qualityand accuracy. Pre-processing corpora to reduce syntactic variability proved tobe a good strategy for increasing the utility of the trained vector models.CONCLUSIONS: Word2vec is a very efficient implementation for computing vectorrepresentations and for its ability to identify relationships in textual datawithout any prior domain knowledge. We found that the ranking and retrievedresults generated by word2vec were not of sufficient quality for automaticpopulation of knowledge bases and ontologies, but could serve as a startingpoint for further manual curation.

Applying deep learning techniques on medical corpora from the World Wide Web: a prototypical system and evaluation