Misspellings in clinical free text present potential challenges to pharmacovigilance tasks, such as monitoring for potential ineffective treatment of drug-resistant infections. We developed a novel method using Word2Vec, Levenshtein edit distance constraints, and a customized lexicon to identify correct and misspelled pharmaceutical word forms. We processed a large corpus of clinical notes in a real-world pharmacovigilance task, achieving positive predictive values of 0.929 and 0.909 in identifying valid misspellings and correct spellings, respectively, and negative predictive values of 0.994 and 0.333 as assessments where the program did not produce output. In a specific Methicillin-Resistant Staphylococcus Aureus use case, the method identified 9,815 additional instances in the corpus for potential inaffective drug administration inspection. The findings suggest that this method could potentially achieve satisfactory results for other pharmacovigilance tasks.
Keywords: Machine Learning; Natural Language Processing.