Julien Souvignet

Adverse Drug Reaction Identification and Extraction in Social Media: A Scoping Review

Background The underreporting of adverse drug reactions (ADRs) through traditional reporting channels is a limitation in the efficiency of the current pharmacovigilance system. Patients’ experiences with drugs that they report on social media represent a new source of data that may have some value in postmarketing safety surveillance. Objective A scoping review was undertaken to explore the breadth of evidence about the use of social media as a new source of knowledge for pharmacovigilance. Methods Daubt et al’s recommendations for scoping reviews were followed. The research questions were as follows: How can social media be used as a data source for postmarketing drug surveillance? What are the available methods for extracting data? What are the different ways to use these data? We queried PubMed, Embase, and Google Scholar to extract relevant articles that were published before June 2014 and with no lower date limit. Two pairs of reviewers independently screened the selected studies and proposed two themes of review: manual ADR identification (theme 1) and automated ADR extraction from social media (theme 2). Descriptive characteristics were collected from the publications to create a database for themes 1 and 2. Results Of the 1032 citations from PubMed and Embase, 11 were relevant to the research question. An additional 13 citations were added after further research on the Internet and in reference lists. Themes 1 and 2 explored 11 and 13 articles, respectively. Ways of approaching the use of social media as a pharmacovigilance data source were identified. Conclusions This scoping review noted multiple methods for identifying target data, extracting them, and evaluating the quality of medical information from social media. It also showed some remaining gaps in the field. Studies related to the identification theme usually failed to accurately assess the completeness, quality, and reliability of the data that were analyzed from social media. Regarding extraction, no study proposed a generic approach to easily adding a new site or data source. Additional studies are required to precisely determine the role of social media in the pharmacovigilance system.

OntoADR a semantic resource describing adverse drug reactions to support searching, coding, and information retrieval

INTRODUCTION Efficient searching and coding in databases that use terminological resources requires that they support efficient data retrieval. The Medical Dictionary for Regulatory Activities (MedDRA) is a reference terminology for several countries and organizations to code adverse drug reactions (ADRs) for pharmacovigilance. Ontologies that are available in the medical domain provide several advantages such as reasoning to improve data retrieval. The field of pharmacovigilance does not yet benefit from a fully operational ontology to formally represent the MedDRA terms. Our objective was to build a semantic resource based on formal description logic to improve MedDRA term retrieval and aid the generation of on-demand custom groupings by appropriately and efficiently selecting terms: OntoADR. METHODS The method consists of the following steps: (1) mapping between MedDRA terms and SNOMED-CT, (2) generation of semantic definitions using semi-automatic methods, (3) storage of the resource and (4) manual curation by pharmacovigilance experts. RESULTS We built a semantic resource for ADRs enabling a new type of semantics-based term search. OntoADR adds new search capabilities relative to previous approaches, overcoming the usual limitations of computation using lightweight description logic, such as the intractability of unions or negation queries, bringing it closer to user needs. Our automated approach for defining MedDRA terms enabled the association of at least one defining relationship with 67% of preferred terms. The curation work performed on our sample showed an error level of 14% for this automated approach. We tested OntoADR in practice, which allowed us to build custom groupings for several medical topics of interest. DISCUSSION The methods we describe in this article could be adapted and extended to other terminologies which do not benefit from a formal semantic representation, thus enabling better data retrieval performance. Our custom groupings of MedDRA terms were used while performing signal detection, which suggests that the graphical user interface we are currently implementing to process OntoADR could be usefully integrated into specialized pharmacovigilance software that rely on MedDRA.

Evaluating Twitter as a complementary data source for pharmacovigilance

ABSTRACT Background: Social media are currently considered as a potential complementary source of knowledge for drug safety surveillance. Our primary objective was to estimate the frequency of adverse drug reactions (ADRs) experienced by Twitter users. Our secondary objective was to determine whether tweets constitute a valuable and informative source of data for pharmacovigilance purposes, despite limitations on character number per tweet. Research design and methods: We selected a list of 33 drugs subject to careful monitoring due to safety concern in France and Europe, and extracted tweets using the streaming API from 30 September 2014 to 5 April 2015. Two pharmacovigilance centers classified these tweets manually as potential ADR case reports. Results: Among 10,534 tweets, 848 (8.05%) implied or mentioned an ADR without meeting the four FDA criteria required for reporting an ADR, and 289 (2.74%) tweets were classified as ‘case reports.’ Among them 20 (7.27%) tweets mentioned an unexpected ADR and 33 (11.42%) tweets mentioned a serious ADR. Conclusions: With the use of dedicated tools, Twitter could become a complementary source of information for pharmacovigilance, despite a major limitation regarding causality assessment of ADRs in individual tweets, which may improve with the new limitation to 280 characters per tweet.

MedDRA® automated term groupings using OntoADR: evaluation with upper gastrointestinal bleedings

ABSTRACT Objective: To propose a method to build customized sets of MedDRA terms for the description of a medical condition. We illustrate this method with upper gastrointestinal bleedings (UGIB). Research design and methods: We created a broad list of MedDRA terms related to UGIB and defined a gold standard with the help of experts. MedDRA terms were formally described in a semantic resource named OntoADR. We report the use of two semantic queries that automatically select candidate terms for UGIB. Query 1 is a combination of two SNOMED CT concepts describing both morphology ‘Hemorrhage’ and finding site ‘Upper digestive tract structure’. Query 2 complements Query 1 by taking into account MedDRA terms associated to SNOMED CT concepts describing clinical manifestations ‘Melena’ or ‘Hematemesis’. Results: We compared terms in queries and our gold standard achieving a recall of 71.0% and a precision of 81.4% for query 1 (F1 score 0.76); and a recall of 96.7% and a precision of 77.0% for query 2 (F1 score 0.86). Conclusions: Our results demonstrate the feasibility of applying knowledge engineering techniques for building customized sets of MedDRA terms. Additional work is necessary to improve precision and recall, and confirm the interest of the proposed strategy.