Dr GOOGLE , promu lanceur d’alerte par la FDA

L’information n’est pas passée inaperçue sur les réseaux sociaux à propos du moteur de recherche Google qui pourrait aider l’agence américaine du médicament (FDA) à détecter les effets secondaires des médicaments. Le Figaro Web &Tech y voit de l’espionnage mais ne s’agit-il pas plutôt d’un pas de plus vers l’hégémonie de l’algorithme, de l’usage des métadonnées pour une meilleure utilisation des signaux en pharmacovigilance ? Pour lire l’article d’Evelyne Pierron Pour lire l’article de Bloomberg

Read More »

FDA taps PatientsLikeMe to test the waters of social media adverse event reporting

Online patient community PatientsLikeMe has found another partner for its massive repository of patient-generated data on health conditions and treatments, but it’s not another pharma company or retail pharmacy. PatientsLikeMe has announced a research partnership with the FDA: The agency will assess the platform’s feasibility as a way to generate adverse event reports, which the FDA uses to regulate drugs after their release into the market. To read the article by mobihealthnews

Read More »

Social Media in Pharmacovigilance: Europe starts moving

Years behind USA, Europe starts to grow in its awareness on the potential use of social media analytics as source of hints for detecting unexpected drug effects after market approval. Independent activities have been launched in 2013-2014 from different founding bodies and with different stakeholders, with strong EU footprint, of which hereafter just few are reported. EU IMI Project WEB-RADR ( web-radr.eu), amining at mobile application and automatic text mining for identification of potential adverse drug events. French-founded ADR-Prism ( adr-prism.com), with initial findings not yet published however first year report to be appearing soon. To read the article by …

Read More »

New system for detecting adverse effects of medications using social media

Researchers at Carlos III Universidad de Madrid have developed a system for detecting adverse effects of pharmaceutical drugs by tracking information generated by patients on specialized blogs or social networks such as Twitter in real time. The prototype, created by these scientists within the framework of the European research Project TrendMiner, makes it possible to analyze the comments on social media by using natural language processing techniques (NLP). Thanks to these techniques, patients’ colloquial descriptions are « translated » into manageable data in comparatives studies which allow us to identify patterns and trends. » This data may also be combined with data from …

Read More »