Exploration of Adverse Drug Reactions in Semantic Vector Space Models of Clinical Text

A novel method for identifying potential side-effects to medications through largescale analysis of clinical data is here introduced and evaluated. By calculating distributional similarities for medication-symptom pairs based on co-occurrence information in a large clinical corpus, many known adverse drug reactions are successfully identified. These preliminary results suggest that semantic vector space models of clinical text could also be used to generate hypotheses about potentially unknown adverse drug reactions. In the best model, 50% of the terms in a list of twenty are considered to be conceivable side-effects. Among the medicationsymptom pairs, however, diagnostic indications and terms related to the medication in other ways also appear. These relations need to be distinguished in a more refined method for detecting adverse drug reactions.