Abstract
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 mining and 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 methylphenidate in this corpus.Methods: We applied text mining methods based on named entity recognition and relation extraction in the corpus, followed by signal detection using proportional reporting ratio (PRR). We also used topic modeling based on the Correlated Topic Model to obtain the list of the matics in the corpus and classify the messages based on their topics.Results: We automatically identified 3443 posts about methylphenidate published between 2007 and 2016, among which 61 adverse drug reactions (ADR) were automatically detected. Two pharmacovigilance experts 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 modeling showed 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 recognition combined with signal detection and topic modeling have demonstrated their complementarity in mining social media data. An in-depth analysis focused on methylphenidate showed that this approach was able to detect potential signals and to provide better understanding of patients' behaviors regarding drugs, including misuse.
Highlights
Patients use social media to seek information, to receive advice and support from other Internet users in order to better manage their own health care and improve their quality of life (Lamas et al, 2016)
Applying proportional reporting ratio (PRR), 67% of the adverse drug reactions (ADR) were signals, including most of the neuro-psychiatric symptoms and palpitations
Named entity recognition combined with signal detection and topic modeling have demonstrated their complementarity in mining social media data
Summary
Patients use social media to seek information, to receive advice and support from other Internet users in order to better manage their own health care and improve their quality of life (Lamas et al, 2016). 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. Referred as pharmacovigilance, focuses primarily on ADRs, which are defined as “a response to a drug which is noxious and unintended3.” It encompasses medication errors, misuse, overdose and abuse (World Health Organization, 1972). Several authors have reached to the conclusion that social media listening is an important tool to augment post-marketing safety surveillance (Powell et al, 2016; Koutkias et al, 2017) These authors consider that much work is needed to determine the best methods for using this data source. In the rest of this article, we will consider “misuse” in its broader meaning, which encompasses the definition provided by the World Health Organization (WHO), i.e., the use of a substance for a purpose non consistent with legal or medical guidelines (like nonmedical use), and the FDA’s one i.e., off-label use (Anderson et al, 2017)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
