Clinical presentation through social media platforms


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PV has shown to be effective at identifying post-marketing adverse medication occurrences (ADE). Natural language processing (NLP) software was previously employed in study to extract unstructured texts pertinent to ADEs. However, sentences devoid of context make these algorithms less effective. Our goal was to create and test aTarantula, an unique NLP tool that uses an aggregated lexicon and a context-aware machine learning algorithm to identify pre-existing ADEs in social media.

In order to extract contextual data from three warfarin-using patient forums (MedHelp, MedsChat, and PatientInfo), aTarantula used FastText embeddings and an aggregated lexicon. From the UMLS and FAERS databases, the lexicon used warfarin package inserts and synonyms of warfarin ADEs. Three clinical pharmacists manually refined and validated the data once it had been placed on SQLite.

The most common organ systems for ADE reports were multiple organ systems (1.50%), followed by CNS side effects (1.19%). The least frequent side effect, lymphatic system ADEs, was observed in 0.09% of cases. Between patient-reported information from the forums and FAERS, the overall Spearman rank correlation coefficient was 0.19. The sensitivity and specificity of Tarantula were shown to be 84.2% and 98%, respectively, by pharmacist validation. Our findings were manually confirmed by three clinical pharmacists. Finally, we developed a lexicon that has been combined for social media ADE mining.

We were able to use aTarantula, a machine-learning algorithm based on artificial intelligence, to automatically extract ADEs relevant to warfarin from online social discussion forums. Our research demonstrates that a Tarantula can be used to find ADEs. On the varied dataset, future researchers can validate aTarantula.