{"id":10171,"date":"2019-11-19T14:53:14","date_gmt":"2019-11-19T14:53:14","guid":{"rendered":"https:\/\/eurekainfomed.it\/?p=10171"},"modified":"2026-07-28T13:35:06","modified_gmt":"2026-07-28T13:35:06","slug":"adr-prism-un-approccio-rigoroso-alla-farmacovigilanza-nei-social-media","status":"publish","type":"post","link":"https:\/\/eurekainfomed.it\/en\/adr-prism-un-approccio-rigoroso-alla-farmacovigilanza-nei-social-media\/","title":{"rendered":"ADR-PRISM: A Rigorous Approach to Pharmacovigilance in Social Media"},"content":{"rendered":"<p>Over the last two decades, <strong>the internet and social media<\/strong> have become an integral part of everyday life, evolving into important <strong>platforms for searching and sharing medical information<\/strong>. This transformation has also made them a valuable source of pharmacovigilance data. However, effectively using these data requires the development of <strong>Natural Language Processing (NLP)<\/strong> tools and machine-learning systems capable of identifying and extracting potential adverse drug reactions (ADRs) from the vast number of online discussions taking place every day.<\/p>\n<div>\n<p>The development of such technologies requires a <strong>highly rigorous methodology<\/strong> to ensure that the results obtained are reliable enough to support decision-making in healthcare and drug safety.<\/p>\n<h4>The French ADR-PRISM Project<\/h4>\n<p>The French Ministry of Industry recently launched the <strong>Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM)<\/strong> project with the objective of developing a new tool for indexing and extracting suspected ADRs from online sources.<\/p>\n<p><strong>The complexity of the project required the involvement of multiple stakeholders<\/strong>, including a software company specializing in text mining, a pharmacoepidemiology company, three research groups with expertise in statistics and health informatics, and two pharmacovigilance experts.<\/p>\n<p>Once the software had been developed, the <strong>ADR-PRISM consortium designed a protocol similar to that used in clinical trials to evaluate the tool\u2019s performance<\/strong>, comparing its results against a gold standard consisting of manual annotations performed by two experts in medical terminology.<\/p>\n<h4>A standardized protocol for software validation<\/h4>\n<p>The effectiveness of the program, called <strong>Skill Cartridge<\/strong>, was tested on a limited number of records retrospectively collected from posts published in the <strong>Kappa Sant\u00e9 Detect&#8217;<\/strong> database between January 2007 and October 2016.<\/p>\n<p>According to the study protocol, only posts containing <strong>at least one drug name and one adverse event<\/strong> included in a predefined list developed by a panel of experts were selected. Using the <strong>Racine Pharma<\/strong> and <strong>MedDRA<\/strong> dictionaries to map medicinal products and adverse reactions respectively, the software was required to classify each co-occurrence of a drug and an adverse event as a <strong>positive ADR<\/strong>, <strong>negative ADR<\/strong>, or <strong>no ADR<\/strong>.<\/p>\n<p>The performance of <strong>Skill Cartridge<\/strong> was then compared with the gold standard by statistically evaluating its precision, recall, and the impact of external factors\u2014such as language granularity and the inherent limitations of natural language\u2014on the results.<\/p>\n<h4>Strengths of skill cartridge<\/h4>\n<p>Several aspects of the ADR-PRISM project represent significant strengths. These include the <strong>definition and validation of a gold standard<\/strong>, the <strong>a priori calculation of sample size<\/strong> to ensure adequate statistical power, the selection of <strong>comprehensive and expressive reference dictionaries<\/strong>, and the implementation of sentence-structure rules capable of excluding non-informative statements.<\/p>\n<p>The approach adopted by the ADR-PRISM consortium provides an example of the <strong>scientific rigor and methodological robustness<\/strong> that should be incorporated into studies of online pharmacovigilance. Such rigor is essential for transforming ADR extraction software\u2014still a relatively emerging technology\u2014into an effective and reliable tool for post-marketing pharmacovigilance.<\/p>\n<p>By applying structured validation methods and robust performance assessments, projects such as ADR-PRISM contribute to the development of innovative approaches for monitoring drug safety in an increasingly digital environment.<\/p>\n<\/div>\n<p><a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31066711\">Arnoux-Guenegou A, Girardeau Y, Chen X, Deldossi M, Aboukhamis R, Faviez C, Dahamna B, Karapetiantz P, Guillemin-Lanne S, Lillo-Le Lou\u00ebt A, Texier N, Burgun A, Katsahian S. The Adverse Drug Reactions From Patient Reports in Social Media Project: Protocol for an Evaluation Against a Gold Standard. JMIR Res Protoc. 2019 May 7;8(5):e11448. doi: 10.2196\/11448.<\/a><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Over the last two decades, the internet and social media have become an integral part of everyday life, evolving into important platforms for searching and sharing medical information. This transformation&#8230;<\/p>\n","protected":false},"author":9,"featured_media":10184,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[25,4,1],"tags":[],"class_list":["post-10171","post","type-post","status-publish","format-standard","has-post-thumbnail","category-pharmacovigilance","category-news","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>ADR-PRISM: Approach to Pharmacovigilance in Social Media<\/title>\n<meta name=\"description\" content=\"The ADR-PRISM project uses natural language processing and scientific validation methods to identify adverse drug reactions from social media\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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