Publications by authors named "Cédric Bousquet"

64 Publications

How to interact with medical terminologies? Formative usability evaluations comparing three approaches for supporting the use of MedDRA by pharmacovigilance specialists.

BMC Med Inform Decis Mak 2020 10 9;20(1):261. Epub 2020 Oct 9.

Laboratoire d'informatique médicale et d'ingénierie des Connaissances en e-santé, LIMICS, Sorbonne Université, Inserm, Université Paris 13, 75006, Paris, France.

Background: Medical terminologies are commonly used in medicine. For instance, to answer a pharmacovigilance question, pharmacovigilance specialists (PVS) search in a pharmacovigilance database for reports in relation to a given drug. To do that, they first need to identify all MedDRA terms that might have been used to code an adverse reaction in the database, but terms may be numerous and difficult to select as they may belong to different parts of the hierarchy. In previous studies, three tools have been developed to help PVS identify and group all relevant MedDRA terms using three different approaches: forms, structured query-builder, and icons. Yet, a poor usability of the tools may increase PVS' workload and reduce their performance. This study aims to evaluate, compare and improve the three tools during two rounds of formative usability evaluation.

Methods: First, a cognitive walkthrough was performed. Based on the design recommendations obtained from this evaluation, designers made modifications to their tools to improve usability. Once this re-engineering phase completed, six PVS took part in a usability test: difficulties, errors and verbalizations during their interaction with the three tools were collected. Their satisfaction was measured through the System Usability Scale. The design recommendations issued from the tests were used to adapt the tools.

Results: All tools had usability problems related to the lack of guidance in the graphical user interface (e.g., unintuitive labels). In two tools, the use of the SNOMED CT to find MedDRA terms hampered their use because French PVS were not used to it. For the most obvious and common terms, the icons-based interface would appear to be more useful. For the less frequently used MedDRA terms or those distributed in different parts of the hierarchy, the structured query-builder would be preferable thanks to its great power and flexibility. The form-based tool seems to be a compromise.

Conclusion: These evaluations made it possible to identify the strengths of each tool but also their weaknesses to address them before further evaluation. Next step is to assess the acceptability of tools and the expressiveness of their results to help identify and group MedDRA terms.
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http://dx.doi.org/10.1186/s12911-020-01280-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7547416PMC
October 2020

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

Stud Health Technol Inform 2020 Jun;272:417-420

Inserm, Sorbonne Université, université Paris 13, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé, LIMICS, F-75006 Paris, France.

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.
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http://dx.doi.org/10.3233/SHTI200584DOI Listing
June 2020

Classification of the Severity of Adverse Drugs Reactions.

Stud Health Technol Inform 2020 Jun;270:1227-1228

Sorbonne Université, INSERM, Université Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, Paris, France.

This poster presents a non-exhaustive study of machine learning classification algorithms on pharmacovigilance data. In this study, we have taken into account the patient's clinical data such as medical history, medications taken and their indications for prescriptions, and the observed side effects. From these elements we determine whether the patient case is considered serious or not. We show the performances of the different algorithms by their precision, recall and accuracy as well as their learning curves.
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http://dx.doi.org/10.3233/SHTI200375DOI Listing
June 2020

Integrating the Comparative Toxicogenomic Database in a Human Pharmacogenomic Resource.

Stud Health Technol Inform 2020 Jun;270:267-271

Public Health and Medical Information Unit, University Hospital of Saint Étienne, Saint Étienne, France.

Information relevant to pharmacogenomics studies is available in several open databases, which makes it difficult to synthetize the available data. Within the PractikPharma project, several databases were integrated to PGxLOD, a resource dedicated to the generation and verification of pharmacogenomic influence on drug responses. The Comparative Toxicogenomic Database (CTD) describes the toxic effects of many chemicals on living species based on the literature. Since drugs are peculiar chemicals and side effects are peculiar toxic effects, we aimed at extracting information from CTD that matches drug side effects in the human specie.
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http://dx.doi.org/10.3233/SHTI200164DOI Listing
June 2020

Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med Project.

Drug Saf 2020 09;43(9):835-851

Laboratoire d'informatique médicale et d'ingénierie des Connaissances en e-santé, LIMICS, Sorbonne Université, Inserm, Université Paris 13, 75006, Paris, France.

The large-scale use of social media by the population has gained the attention of stakeholders and researchers in various fields. In the domain of pharmacovigilance, this new resource was initially considered as an opportunity to overcome underreporting and monitor the safety of drugs in real time in close connection with patients. Research is still required to overcome technical challenges related to data extraction, annotation, and filtering, and there is not yet a clear consensus concerning the systematic exploration and use of social media in pharmacovigilance. Although the literature has mainly considered signal detection, the potential value of social media to support other pharmacovigilance activities should also be explored. The objective of this paper is to present the main findings and subsequent recommendations from the French research project Vigi4Med, which evaluated the use of social media, mainly web forums, for pharmacovigilance activities. This project included an analysis of the existing literature, which contributed to the recommendations presented herein. The recommendations are categorized into three categories: ethical (related to privacy, confidentiality, and follow-up), qualitative (related to the quality of the information), and quantitative (related to statistical analysis). We argue that the progress in information technology and the societal need to consider patients' experiences should motivate future research on social media surveillance for the reinforcement of classical pharmacovigilance.
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http://dx.doi.org/10.1007/s40264-020-00951-2DOI Listing
September 2020

PGxCorpus, a manually annotated corpus for pharmacogenomics.

Sci Data 2020 01 2;7(1). Epub 2020 Jan 2.

Université de Lorraine, CNRS, Inria, LORIA, Nancy, France.

Pharmacogenomics (PGx) studies how individual gene variations impact drug response phenotypes, which makes PGx-related knowledge a key component towards precision medicine. A significant part of the state-of-the-art knowledge in PGx is accumulated in scientific publications, where it is hardly reusable by humans or software. Natural language processing techniques have been developed to guide experts who curate this amount of knowledge. But existing works are limited by the absence of a high quality annotated corpus focusing on PGx domain. In particular, this absence restricts the use of supervised machine learning. This article introduces PGxCorpus, a manually annotated corpus, designed to fill this gap and to enable the automatic extraction of PGx relationships from text. It comprises 945 sentences from 911 PubMed abstracts, annotated with PGx entities of interest (mainly gene variations, genes, drugs and phenotypes), and relationships between those. In this article, we present the corpus itself, its construction and a baseline experiment that illustrates how it may be leveraged to synthesize and summarize PGx knowledge.
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http://dx.doi.org/10.1038/s41597-019-0342-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6940385PMC
January 2020

Pharmacology and social media: Potentials and biases of web forums for drug mention analysis-case study of France.

Health Informatics J 2020 06 30;26(2):1253-1272. Epub 2019 Sep 30.

Sorbonne Université and Université Paris 13, France; CHU University Hospital of Saint-Etienne, France.

The aim of this study is to analyze drug mentions in web forums to evaluate the utility of this data source for drug post-marketing studies. We automatically annotated over 60 million posts extracted from 21 French web forums. Drug mentions detected in this corpus were matched to drug names in a French drug database (Theriaque). Our analysis showed that a high proportion of the most frequent drug mentions in the selected web forums correspond to drugs that are usually prescribed to young women, such as combined oral contraceptives. The most mentioned drugs in our corpus correlated weakly to the most prescribed drugs in France but seemed to be influenced by events widely reported in traditional media. In this article, we conclude that web forums have high potential for post-marketing drug-related studies, such as pharmacovigilance, and observation of drug utilization. However, the bias related to forum selection and the corresponding population representativeness should always be taken into account.
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http://dx.doi.org/10.1177/1460458219865128DOI Listing
June 2020

Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties.

Front Pharmacol 2019 10;10:975. Epub 2019 Sep 10.

EA 2223 Costech (Connaissance, Organisation et Systèmes Techniques), Centre de Recherche, Sorbonne Universités, Université de technologie de Compiègne, Compiègne, France.

Formal definitions allow selecting terms (e.g., identifying all terms related to "Infectious disease" using the query "has causative agent organism") and terminological reasoning (e.g., "hepatitis B" is a "hepatitis" and is an "infectious disease"). However, the standard international terminology Medical Dictionary for Regulatory Activities (MedDRA) used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. Our objective was to evaluate the potential of reuse of ontological and non-ontological resources for generating such definitions for MedDRA. We developed several methods that collectively allow a semiautomatic semantic enrichment of MedDRA: 1) using MedDRA-to-SNOMED Clinical Terms (SNOMED CT) mappings (available in the Unified Medical Language System metathesaurus or other mapping resources, e.g., the MedDRA preferred term "hepatitis B" is associated to the SNOMED CT concept "type B viral hepatitis") to extract term definitions (e.g., "hepatitis B" is associated with the following properties: has finding site liver structure, has associated morphology inflammation morphology, and has causative agent hepatitis B virus); 2) using MedDRA labels and lexical/syntactic methods for automatic decomposition of complex MedDRA terms (e.g., the MedDRA systems organ class "blood and lymphatic system disorders" is decomposed in blood system disorders and lymphatic system disorders) or automatic suggestions of properties (e.g., the string "cyclic" in preferred term "cyclic neutropenia" leads to the property has clinical course cyclic). The Unified Medical Language System metathesaurus was the main ontological resource reusable for generating formal definitions for MedDRA terms. The non-ontological resources (another mapping resource provided by Nadkarni and Darer in 2010 and MedDRA labels) allowed defining few additional preferred terms. While the Ci4SeR tool helped the curator to define 1,935 terms by suggesting potential supplemental relations based on the parents' and siblings' semantic definition, defining manually all MedDRA terms remains expensive in time. Several ontological and non-ontological resources are available for associating MedDRA terms to SNOMED CT concepts with semantic properties, but providing manual definitions is still necessary. The ontology of adverse events is a possible alternative but does not cover all MedDRA terms either. Perspectives are to implement more efficient techniques to find more logical relations between SNOMED CT and MedDRA in an automated way.
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http://dx.doi.org/10.3389/fphar.2019.00975DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6747929PMC
September 2019

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

Stud Health Technol Inform 2019 Aug;264:964-968

Sorbonne Université, Inserm, université Paris 13, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé, LIMICS, F-75006 Paris, France.

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.
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http://dx.doi.org/10.3233/SHTI190367DOI Listing
August 2019

Automated Control of Codes Accuracy in Case-Mix Databases by Evaluating Coherence with Available Information in the Electronic Health Record.

Stud Health Technol Inform 2019 Aug;264:551-555

Public Health and Medical Information Department, University Hospital Center of Saint Etienne, France.

Coding accuracy in case-mix databases enables efficient funding of health facilities and accurate epidemiological statistics based on patients' stays information. We assume that the data collected in the electronic health record, especially drug prescriptions and medical reports are relevant for checking the consistency of the coding of diagnoses. We evaluated a new coding control tool, "TOLBIAC control", embedded in the Web100T coding assistant. This tool interacts with the Vidal Application Programming Interface and the electronic health record of the University Hospital of Saint-Etienne. The micro-average F-measure was 0.76 for drug prescriptions and 0.55 for free text medical reports. This initial evaluation has revealed that drug prescriptions in EHRs can successfully be used to develop an automated ICD-10 code-control tool. Nevertheless the "TOLBIAC control" tool is not yet fully effective for widespread use because of its limited performance in text analysis, a feature that is currently undergoing improvements.
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http://dx.doi.org/10.3233/SHTI190283DOI Listing
August 2019

An Iconic Approach to the Browsing of Medical Terminologies.

Stud Health Technol Inform 2019 Aug;264:213-217

LIMICS, Université Paris 13, Sorbonne Paris Cité, 93017 Bobigny, INSERM UMRS 1142, Sorbonne Université, Paris, France.

Medical terminologies are the basis of interoperability in medicine. They allow connecting the various systems and data and facilitate searches in databases. An example is the MedDRA terminology, used in particular for coding drug adverse events. However, these terminologies are often complex and involve a huge number of terms. Consequently, it is difficult to browse them or 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 to 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.
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http://dx.doi.org/10.3233/SHTI190214DOI Listing
August 2019

[Informativity of French web forums for the evaluation of side effects of baclofen].

Therapie 2019 Dec 31;74(6):569-578. Epub 2019 May 31.

Inserm, laboratoire d'informatique médicale et d'ingéniérie des connaissance en e-santé, Sorbonne université, université Paris 13, 15, rue de l'école de médicine, 75006 Paris, France.

Objective: To evaluate the informativity, quality of French discussion forums for evaluation of baclofen safety.

Methods: We evaluated the quality of potential pharmacovigilance case reports associated to baclofen in 22 French discussion forums. We compared the informativity concerning the patient, treatment, seriousness and expectedness of adverse events described on these posts, with similar information coded in case reports from the French pharmacovigilance database (FPVD).

Results: A total of 782 potential case reports were identified among 2621 French language forums' posts. Cases in the FPVD were significantly more informative than web forums' posts for patient information (3%/6% vs. 88% for the age/class of age and 46% vs. 99% for the gender), treatment duration (9% vs. 24%) and outcome of the ADR (1% vs. 64%). But both indication and dose were more frequently retrieved in forums than in the FPVD (67% vs. 24% and 27% vs. 9%, respectively). Cases from web forums were significantly more frequently non-serious than the FPVD's ones (38% vs. 0.7%). Adverse events were significantly more often unexpected in forums than in the FPVD (43.8% vs. 11.6%).

Conclusion: Indication and posology were more often documented in posts than in case reports which makes forums an interesting resource for monitoring use of baclofen. While posts contain more unexpected events, informativity is low which makes causality assessment difficult. Nevertheless, we consider forums as a secondary, but complementary source for pharmacovigilance about baclofen.
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http://dx.doi.org/10.1016/j.therap.2019.05.003DOI Listing
December 2019

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.

Front Pharmacol 2019 17;10:415. Epub 2019 May 17.

Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing "knowledge-intensive" systems, depending on a conceptual "knowledge" schema and some kind of "reasoning" process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.
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http://dx.doi.org/10.3389/fphar.2019.00415DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6533857PMC
May 2019

Semantic Queries Expedite MedDRA Terms Selection Thanks to a Dedicated User Interface: A Pilot Study on Five Medical Conditions.

Front Pharmacol 2019 6;10:50. Epub 2019 Feb 6.

Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, INSERM, Sorbonne Université, Université Paris 13, Paris, France.

Searching into the MedDRA terminology is usually limited to a hierarchical search, and/or a string search. Our objective was to compare user performances when using a new kind of user interface enabling semantic queries versus classical methods, and evaluating term selection improvement in MedDRA. We implemented a forms-based web interface: OntoADR Query Tools (OQT). It relies on OntoADR, a formal resource describing MedDRA terms using SNOMED CT concepts and corresponding semantic relations, enabling terminological reasoning. We then compared time spent on five examples of medical conditions using OQT or the MedDRA web-based browser (MWB), and precision and recall of the term selection. OntoADR Query Tools allows the user to search in MedDRA: One may enter search criteria by selecting one semantic property from a dropdown list and one or more SNOMED CT concepts related to the range of the chosen property. The user is assisted in building his query: he can add criteria and combine them. Then, the interface displays the set of MedDRA terms matching the query. Meanwhile, on average, the time spent on OQT (about 4 min 30 s) is significantly lower (-35%; < 0.001) than time spent on MWB (about 7 min). The results of the System Usability Scale (SUS) gave a score of 62.19 for OQT (rated as good). We also demonstrated increased precision (+27%; = 0.01) and recall (+34%; = 0.02). Computed "performance" (correct terms found per minute) is more than three times better with OQT than with MWB. This pilot study establishes the feasibility of our approach based on our initial assumption: performing MedDRA queries on the five selected medical conditions, using terminological reasoning, expedites term selection, and improves search capabilities for pharmacovigilance end users. Evaluation with a larger number of users and medical conditions are required in order to establish if OQT is appropriate for the needs of different user profiles, and to check if conclusions can be extended to other kinds of medical conditions. The application is currently limited by the non-exhaustive coverage of MedDRA by OntoADR, but nevertheless shows good performance which encourages continuing in the same direction.
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http://dx.doi.org/10.3389/fphar.2019.00050DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374626PMC
February 2019

Modeling Keyword Search Strategy: Analysis of Pharmacovigilance Specialists' Search of MedDRA Terms.

Stud Health Technol Inform 2019 ;257:298-302

Univ. Lille, INSERM, CHU Lille, CIC-IT/Evalab 1403 - Centre d'Investigation Clinique, EA 2694, F-59000 Lille, France.

In the information retrieval task, searching and choosing keywords to form the query is crucial. The present study analyzes and describes the keywords' search strategy into a thesaurus in the field of pharmacovigilance. Two ergonomics experts shadowed 22 pharmacovigilance specialists during their daily work. They focus on the strategies for searching and choosing MedDRA terms to build pharmacovigilance queries. Interviews of four pharmacovigilance specialists completed the observations. Results highlight that, for unusual or complex searches, pharmacovigilance specialists proceed iteratively in three main phases: (i) preparation of a list of terms and of evaluation criteria, (ii) exploration of the MedDRA hierarchy and choice of a term, and (iii) evaluation of the results against the criteria. Overall, the search and the choice of keywords within a thesaurus shares similarity with the information retrieval task and is closely interwoven with the query building process. Based on the results, the paper proposes design specifications for new interfaces supporting the identification of MedDRA terms so that pharmacovigilance reports searches achieve a good level of expressiveness.
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August 2019

Evaluating Twitter as a complementary data source for pharmacovigilance.

Expert Opin Drug Saf 2018 Aug 26;17(8):763-774. Epub 2018 Jul 26.

a Sorbonne Université , UPMC Université Paris 06, UMR_S 1142, LIMICS , Paris , France.

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.
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http://dx.doi.org/10.1080/14740338.2018.1499724DOI Listing
August 2018

Patients' Perception of Privacy of Personal Data, Shared in Online Communities: Are We in the Presence of a Paradox?

Stud Health Technol Inform 2018 ;251:237-240

Facultad de Medicina, Universidad de Chile, Santiago, Chile.

Virtual online communities help people in coping with complex health issues, such as those present in patients suffering chronic diseases. Further research is required in order to clarify the impact of sharing of personal experiences on the perception of privacy and confidentiality by patients. We studied the case of Carenity an online social network created in France in 2011 bringing together 300,000 patients across Europe, and selected patients suffering Multiple Sclerosis. We conducted an exploratory-descriptive survey, and 253 patients completed an online questionnaire. Most participants did not consider that their privacy was threatened when sharing their personal experiences and data associated with their health condition. As common sense prevents one to share information to strangers to ensure privacy, such paradox may be explained by new strategies to face challenges imposed by chronic conditions disease, where sharing personal experiences may be considered as a complementary source of social support by patients.
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November 2018

Mining Patients' Narratives in Social Media for Pharmacovigilance: Adverse Effects and Misuse of Methylphenidate.

Front Pharmacol 2018 24;9:541. Epub 2018 May 24.

UMRS 1138, équipe 22, Institut National de la Santé et de la Recherche Médicale, Centre de Recherche des Cordeliers, Université Paris Descartes, Paris, France.

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. 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. 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. 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 , but also . Cases of misuse were also identified in this corpus, including recreational use and abuse. 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.
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http://dx.doi.org/10.3389/fphar.2018.00541DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978246PMC
May 2018

Descriptions of Adverse Drug Reactions Are Less Informative in Forums Than in the French Pharmacovigilance Database but Provide More Unexpected Reactions.

Front Pharmacol 2018 1;9:439. Epub 2018 May 1.

Sorbonne Université, INSERM, Université Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, Paris, France.

Social media have drawn attention for their potential use in Pharmacovigilance. Recent work showed that it is possible to extract information concerning adverse drug reactions (ADRs) from posts in social media. The main objective of the Vigi4MED project was to evaluate the relevance and quality of the information shared by patients on web forums about drug safety and its potential utility for pharmacovigilance. After selecting websites of interest, we manually evaluated the relevance of the content of posts for pharmacovigilance related to six drugs (agomelatine, baclofen, duloxetine, exenatide, strontium ranelate, and tetrazepam). We compared forums to the French Pharmacovigilance Database (FPVD) to (1) evaluate whether they contained relevant information to characterize a pharmacovigilance case report (patient's age and sex; treatment indication, dose and duration; time-to-onset (TTO) and outcome of the ADR, and drug dechallenge and rechallenge) and (2) perform impact analysis (nature, seriousness, unexpectedness, and outcome of the ADR). The cases in the FPVD were significantly more informative than posts in forums for patient description (age, sex), treatment description (dose, duration, TTO), and outcome of the ADR, but the indication for the treatment was more often found in forums. Cases were more often serious in the FPVD than in forums (46% vs. 4%), but forums more often contained an unexpected ADR than the FPVD (24% vs. 17%). Moreover, 197 unexpected ADRs identified in forums were absent from the FPVD and the distribution of the MedDRA System Organ Classes (SOCs) was different between the two data sources. This study is the first to evaluate if patients' posts may qualify as potential and informative case reports that should be stored in a pharmacovigilance database in the same way as case reports submitted by health professionals. The posts were less informative (except for the indication) and focused on less serious ADRs than the FPVD cases, but more unexpected ADRs were presented in forums than in the FPVD and their SOCs were different. Thus, web forums should be considered as a secondary, but complementary source for pharmacovigilance.
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http://dx.doi.org/10.3389/fphar.2018.00439DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5938397PMC
May 2018

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

Stud Health Technol Inform 2018 ;247:421-425

Sorbonne Université, Inserm, université Paris 13, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé, LIMICS, F-75006 Paris, France.

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.
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June 2018

The Meaning of Patient Empowerment in the Digital Age: The Role of Online Patient-Communities.

Stud Health Technol Inform 2017 ;244:43-47

Facultad de Medicina, Universidad de Santiago, Santiago, Chile.

Traditionally, patient empowerment has been used as a strategy for health promotion. The rise of online communities of patients represents a good example of how patient empowerment occurs, independently of the intervention of existing healthcare providers and insurers, allowing thus a more accurate definition of meaning of this concept. We describe two situations related with the development of health-related social networks: (1) The emergence of a new biomedical research model in which patients lead research, shifting the equilibrium of power from the professionals to research subjects themselves, and (2) The emergence of Lay Crowd-Sourced Expertise in these communities, arising from the daily exchange among patients affected by chronic conditions and their relatives, giving place to a new era of bottom-up data generation, previously unknown in biomedical sciences. We enrich these descriptions by analyzing interviews to key actors of these "on line" communities": Michael Chekroun, founder of "Carenity, France", and Paul Wicks Vice President at "PatientsLikeMe, USA".
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June 2018

The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process.

JMIR Res Protoc 2017 Sep 21;6(9):e179. Epub 2017 Sep 21.

Kappa Santé, Paris, France.

Background: Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture.

Objective: This article aims to describe the current state of advancement of the ADR-PRISM project by focusing on the solutions we have chosen to address these 5 major challenges.

Methods: In this article, we propose methods and describe the advancement of this project on several aspects: (1) a quality driven approach for selecting relevant social media for the extraction of knowledge on potential ADRs, (2) an assessment of ethical issues and French regulation for the analysis of data on social media, (3) an analysis of pharmacovigilance expert requirements when reviewing patient posts on the Internet, (4) an extraction method based on natural language processing, pattern based matching, and selection of relevant medical concepts in reference terminologies, and (5) specifications of a component-based architecture for the monitoring system.

Results: Considering the 5 major challenges, we (1) selected a set of 21 validated criteria for selecting social media to support the extraction of potential ADRs, (2) proposed solutions to guarantee data privacy of patients posting on Internet, (3) took into account pharmacovigilance expert requirements with use case diagrams and scenarios, (4) built domain-specific knowledge resources embeding a lexicon, morphological rules, context rules, semantic rules, syntactic rules, and post-analysis processing, and (5) proposed a component-based architecture that allows storage of big data and accessibility to third-party applications through Web services.

Conclusions: We demonstrated the feasibility of implementing a component-based architecture that allows collection of patient posts on the Internet, near real-time processing of those posts including annotation, and storage in big data structures. In the next steps, we will evaluate the posts identified by the system in social media to clarify the interest and relevance of such approach to improve conventional pharmacovigilance processes based on spontaneous reporting.
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http://dx.doi.org/10.2196/resprot.6463DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629348PMC
September 2017

Vigi4Med Scraper: A Framework for Web Forum Structured Data Extraction and Semantic Representation.

PLoS One 2017 25;12(1):e0169658. Epub 2017 Jan 25.

INSERM, U1142, LIMICS, Paris, France.

The extraction of information from social media is an essential yet complicated step for data analysis in multiple domains. In this paper, we present Vigi4Med Scraper, a generic open source framework for extracting structured data from web forums. Our framework is highly configurable; using a configuration file, the user can freely choose the data to extract from any web forum. The extracted data are anonymized and represented in a semantic structure using Resource Description Framework (RDF) graphs. This representation enables efficient manipulation by data analysis algorithms and allows the collected data to be directly linked to any existing semantic resource. To avoid server overload, an integrated proxy with caching functionality imposes a minimal delay between sequential requests. Vigi4Med Scraper represents the first step of Vigi4Med, a project to detect adverse drug reactions (ADRs) from social networks founded by the French drug safety agency Agence Nationale de Sécurité du Médicament (ANSM). Vigi4Med Scraper has successfully extracted greater than 200 gigabytes of data from the web forums of over 20 different websites.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169658PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5266266PMC
August 2017

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

J Biomed Inform 2016 10 28;63:100-107. Epub 2016 Jun 28.

INSERM, U1142, LIMICS, F-75006 Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1142, LIMICS, F-75006 Paris, France; Université Paris 13, Sorbonne Paris Cité, LIMICS, (UMR_S 1142), F-93430 Villetaneuse, France; SSPIM, CHU University Hospital of Saint Etienne, Saint Etienne, France. Electronic address:

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.
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http://dx.doi.org/10.1016/j.jbi.2016.06.010DOI Listing
October 2016

[Automated grouping of terms associated to cardiac valve fibrosis in MedDRA].

Therapie 2016 Dec 21;71(6):541-552. Epub 2016 Jul 21.

UMR_S 1142, Inserm, LIMICS, Sorbonne universités, UPMC université Paris 06, 75006 Paris, France; Service de santé publique et d'information médicale, hôpital nord, centre hospitalier universitaire de Saint-Etienne, 42270 Saint-Etienne, France.

Aim: To propose an alternative approach for building custom groupings of terms that complements the usual approach based on both hierarchical method (selection of reference groupings in medical dictionary for regulatory activities [MedDRA]) and/or textual method (string search), for case reports extraction from a pharmacovigilance database in response to a safety problem. Here we take cardiac valve fibrosis as an example.

Methods: The list of terms obtained by an automated approach, based on querying ontology of adverse drug reactions (OntoADR), a knowledge base defining MedDRA terms through relationships with systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts, was compared with the reference list consisting of 53 preferred terms obtained by hierarchical and textual method. Two queries were performed on OntoADR by using a dedicated software: OntoADR query tools. Both queries excluded congenital diseases, and included a procedure or an auscultation method performed on cardiac valve structures. Query 1 also considered MedDRA terms related to fibrosis, narrowing or calcification of heart valves, and query 2 MedDRA terms described according to one of these four SNOMED CT terms: "Insufficiency", "Valvular sclerosis", "Heart valve calcification" or "Heart valve stenosis".

Results: The reference grouping consisted of 53 MedDRA preferred terms. Our automated method achieved recall of 79% and precision of 100% for query 1 privileging morphological abnormalities, and recall of 100% and precision of 96% for query 2 privileging functional abnormalities.

Conclusion: An alternative approach to MedDRA reference groupings for building custom groupings is feasible for cardiac valve fibrosis. OntoADR is still in development. Its application to other adverse reactions would require significant work for a knowledge engineer to define every MedDRA term, but such definitions could then be queried as many times as necessary by pharmacovigilance professionals.
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http://dx.doi.org/10.1016/j.therap.2016.06.003DOI Listing
December 2016

Inconsistencies Between Antiparkinsonian Drugs and ICD-10 Codes in Inpatients: A TOLBIAC Project Case Study.

Stud Health Technol Inform 2016 ;228:364-8

INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Université. Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France; Paris University13, Sorbonne Paris City, LIMICS, (UMR_S 1142), F-93430, Villetaneuse, France.

In France, data derived from hospital information systems are adequate to feed the prospective payment system. The consistency between drugs prescribed to patients and their indications could solve difficulties related to the identification of ICD-10 undercoded chronic diseases as the Parkinson Disease. Our goal was to highlight patients' stays mentioning administration of antiparkinsonian drugs and not coded for Parkinson's disease. Our approach was to parameterize tables of associations between ICD-10 codes and drug identifiers in the Web100T® application that collects medical information in our hospital and displays related inconsistencies for patients' stays. Based on acute care patients' stays of the second semester of 2015, we identified 246 patients corresponding to 253 stays, for which 33% of stays were not coded with the ICD-10 G20 code of the Parkinson's disease. The precision of our approach was 29%. Based on these data we predict roughly 84 patient stays without mention of Parkinson Disease. We plan to extend this study to other drugs and other kinds of data available in the health information system, such as biology or medical devices in order to improve the coding of chronic diseases in our hospital.
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April 2017

Lay Crowd-Sourced Expertise (LCE) and Its Influence on the New Role of Patients: Ethical and Societal Issues.

Stud Health Technol Inform 2016 ;228:80-4

CERMES 3 : INSERM U.988/UMR CNRS 8211/EHESS/Université Paris Descartes, F-94801, Villejuif, France.

The emergence of social media on the Internet allows patients to discuss about their chronic diseases within online communities sharing common interests. This allows patients to gather other patients' experience, and gain new knowledge that is usually not shared by healthcare professionals. In this context, further studies are required on the actual impact of the use of social networks on the quality of life of patients participating in these online communities, focusing on the evolving role and impact of Lay Crowdsourced expertise (LCE) in improving disease management and control. We present a study on a large number of posts from social networks of different online communities. This study allowed us to choose four pathologies, with distinctive characteristics relevant for our future analysis, and to define the themes that will be covered in future work by online questionnaires. The analysis of responses from patients, who volunteer to participate, will help us in exploring how interactions between patients, on these online communities, may help them to gain useful information for managing their conditions and improving their quality of life. Furthermore, we will identify new ethical issues that arise in the sharing of health data.
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April 2017

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

Expert Opin Drug Saf 2016 Sep 15;15(9):1153-61. Epub 2016 Jul 15.

a INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France; Université Paris 13, Sorbonne Paris Cité, LIMICS, (UMR_S 1142) , F-93430 , Villetaneuse , France.

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.
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http://dx.doi.org/10.1080/14740338.2016.1206075DOI Listing
September 2016

Good Signal Detection Practices: Evidence from IMI PROTECT.

Drug Saf 2016 06;39(6):469-90

Uppsala Monitoring Centre, Uppsala, Sweden.

Over a period of 5 years, the Innovative Medicines Initiative PROTECT (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium) project has addressed key research questions relevant to the science of safety signal detection. The results of studies conducted into quantitative signal detection in spontaneous reporting, clinical trial and electronic health records databases are summarised and 39 recommendations have been formulated, many based on comparative analyses across a range of databases (e.g. regulatory, pharmaceutical company). The recommendations point to pragmatic steps that those working in the pharmacovigilance community can take to improve signal detection practices, whether in a national or international agency or in a pharmaceutical company setting. PROTECT has also pointed to areas of potentially fruitful future research and some areas where further effort is likely to yield less.
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http://dx.doi.org/10.1007/s40264-016-0405-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4871909PMC
June 2016