Mining patients' narratives in social media for pharmacovigilance: adverse effects and misuse of methylphenidate

Background: The Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) have recognized social media as a new data source to strengthen their activities regarding drug safety. Objective: Our objective in the ADR-PRISM project was to provide text miningand visualization tools to explore a corpus of posts extracted from social media. We evaluated this approach on a corpus of 21 million posts from five patient forums, and conducted a qualitative analysis of the data available on methylphenidatein this corpus. Methods: We applied text miningmethods based on named entity recognitionand relation extraction in the corpus, followed by signal detection using proportional reporting ratio (PRR). We also used topic modellingbased on the Correlated Topic Modelto obtain the list of thematics in the corpus and classify the messages based on their topics. Results: We automatically identified 3443 posts about methylphenidatepublished between 2007 and 2016, among which 61 adverse drug reactions (ADR) were automatically detected. Two pharmacovigilanceexperts evaluated manually the quality of automatic identification, and a f-measure of 0.57 was reached. Patient’s reports were mainly neuro-psychiatric effects. Applying PRR, 67% of the ADRs were signals, including most of the neuro-psychiatric symptoms but also palpitations. Topic modellingshowed that the most represented topics were related to Childhood and Treatment initiation, but also Side effects. Cases of misuse were also identified in this corpus, including recreational use and abuse. Conclusion: Named entity recognitioncombined with signal detection and topic modellinghave demonstrated their complementarity in mining social media data. An in-depth analysis focused on methylphenidateshowed that this approach was able to detect potential signals and to provide better understanding of patients’ behaviors regarding drugs, including misuse.