Adverse Drug Reaction Mining in Pharmacovigilance data using Formal Concept Analysis

International audienceIn this paper we discuss the problem of extracting and evaluating associations between drugs and adverse effects in pharmacovigilance data. Approaches proposed by the medical informatics community for mining one drug - one effect pairs perform an exhaustive search strategy that precludes from mining high-order associations. Some specificities of pharmacovigilance data prevent from applying pattern mining approaches proposed by the data mining community for similar problems dealing with epidemiological studies. We argue that Formal Concept Analysis (FCA) and concept lattices constitute a suitable framework for both identifying relevant associations, and assisting experts in their evaluation task. Demographic attributes are handled so that the disproportionality of an association is computed w.r.t. the relevant population stratum to prevent confounding. We put the focus on the understandability of the results and provide evaluation facilities for experts. A real case study on a subset of the French spontaneous reporting system shows that the method identifies known adverse drug reactions and some unknown associations that has to be further investigated

Risques iatrogènes des anticoagulants (étude observationnelle à l'hôpital et mesures de minimisation des risques)

PARIS-BIUP (751062107) / SudocSudocFranceF

Signal Detection for Baclofen in Web Forums: A Preliminary Study.

Web forums are proposed as a new complementary source of knowledge to spontaneous reports by patients and healthcare professionals due to underreporting of adverse drug reactions (ADRs). Some authors suggest that signal detection could be a convenient method for gathering mentions of ADRs in patients' posts. Signal detection methods were proposed to mine pharmacovigilance databases, but little is known about their applicability to web forums. We describe a method implementing several traditional decision rules on signal detection with baclofen applied to a set of more than 6 million posts. We then cross-validated four unexpected signals applying a logistic regression method. Most adverse effects (AEs) described in the summary of product characteristics of baclofen were detected by signal detection methods. Some unexpected AEs were too. Therefore, web forums are confirmed as a complementary resource for improving current knowledge in pharmacovigilance by detecting unexpected adverse drug reactions

Comment on “Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project”

International audienc

Discrepancy Between Personal Experience and Negative Opinion with Human Papillomavirus Vaccine in Web Forums.

While vaccines are intended to protect people from infectious diseases, public confidence in vaccination has evolved as patients have reservation about vaccination, with a major concern about its safety. Social media may help regulatory authorities to better understand opposition to vaccination and make informed decisions for better promotion of vaccines' benefits towards the public. Our objective was to explore French web forums for potential pharmacovigilance signals associated with human papillomavirus infections (HPV) vaccines. Among 138 posts associated with a signal randomly chosen for manual review, 29% were actually adverse drug reactions to the vaccine described in clinical studies, and only 2 were personal experiences. Only 14% of the reviewed posts described positive opinion about the vaccine whereas 46% were neutral and 40% were negative. While few personal experiences of adverse reactions were actually reported by users, our case study showed a large proportion of negative opinions

An iconic approach to the browsing of medical terminologies

International audienceMedical terminologies are the basis of interoperability in medicine. They allow connecting the various systems and data, and they facilitate search in databases. An example is the MedDRA terminology, which is used in particular for coding drug adverse events. However, these terminologies are often complex and they involve a huge number of terms. Consequently, it is difficult to browse them or to find the desired terms. Traditional approaches consist of lexical search, with the problems of synonymy and polysemy, or tree-based navigation, but the user often gets "lost" in the tree. Here, we propose a new approach for browsing medical terminologies: the use of pictograms and icons, for formulating the query in complement of a textual search box, and for displaying the search results. We applied this approach to the MedDRA terminology. We present both the methods and search algorithms and the resulting browsing interface, as well as the qualitative opinions of two pharmacovigilance experts

Initial Experiments for Pharmacovigilance Analysis in Social Media using Summaries of Product Characteristics

International audienceWe report initial experiments for analyzing social media through an NLP annotation tool on web posts about medications of current interests (baclofen, levothyroxine and vaccines) and summaries of product characteristics (SPCs). We conducted supervised experiments on a subset of messages annotated by experts according to positive or negative misuse; results ranged from 0.62 to 0.91 of F-score. We also annotated both SPCs and another set of posts to compare MedDRA annotations in each source. A pharmacovigilance expert checked the output and confirmed that entities not found in SCPs might express drug misuse or unknown ADRs

Qualitative and Quantitative Analysis of Web Forums for Adverse Events Detection: "Strontium Ranelate" Case Study.

Social media are proposed as a complementary data source for detection and characterisation of adverse drug reactions. While signal detection algorithms were implemented for generating signals in pharmacovigilance databases, the implementation of a graphical user interface supporting the selection and display of algorithms' results is not documented in the medical literature. Although collecting information on the chronology and the impact of adverse drug reactions is desirable to enable causality and quality assessment of potential signals detected in patients' posts, no tool has been proposed yet to consider such data. We describe here two approaches, and the corresponding tools we implemented for: (1) quantitative approach based on signal detection algorithms, and (2) qualitative approach based on expert review of patient's posts. Future work will focus on implementing other statistical methods, exploring the complementarity of both approaches on a larger scale, and prioritizing the posts to manually evaluate after applying appropriate signal detection methods