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Karapetiantz P, Audeh B, Redjdal A, Tiffet T, Bousquet C, Jaulent MC. Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study. J Med Internet Res 2024; 26:e46176. [PMID: 38888956 PMCID: PMC11220433 DOI: 10.2196/46176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 10/20/2023] [Accepted: 03/12/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND To mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system. Explorations into alternative sources, including electronic patient records and social media, have been undertaken. Nevertheless, social media's potential remains largely untapped in real-world scenarios. OBJECTIVE The challenge faced by regulatory agencies in using social media is primarily attributed to the absence of suitable tools to support decision makers. An effective tool should enable access to information via a graphical user interface, presenting data in a user-friendly manner rather than in their raw form. This interface should offer various visualization options, empowering users to choose representations that best convey the data and facilitate informed decision-making. Thus, this study aims to assess the potential of integrating social media into pharmacovigilance and enhancing decision-making with this novel data source. To achieve this, our objective was to develop and assess a pipeline that processes data from the extraction of web forum posts to the generation of indicators and alerts within a visual and interactive environment. The goal was to create a user-friendly tool that enables regulatory authorities to make better-informed decisions effectively. METHODS To enhance pharmacovigilance efforts, we have devised a pipeline comprising 4 distinct modules, each independently editable, aimed at efficiently analyzing health-related French web forums. These modules were (1) web forums' posts extraction, (2) web forums' posts annotation, (3) statistics and signal detection algorithm, and (4) a graphical user interface (GUI). We showcase the efficacy of the GUI through an illustrative case study involving the introduction of the new formula of Levothyrox in France. This event led to a surge in reports to the French regulatory authority. RESULTS Between January 1, 2017, and February 28, 2021, a total of 2,081,296 posts were extracted from 23 French web forums. These posts contained 437,192 normalized drug-ADE couples, annotated with the Anatomical Therapeutic Chemical (ATC) Classification and Medical Dictionary for Regulatory Activities (MedDRA). The analysis of the Levothyrox new formula revealed a notable pattern. In August 2017, there was a sharp increase in posts related to this medication on social media platforms, which coincided with a substantial uptick in reports submitted by patients to the national regulatory authority during the same period. CONCLUSIONS We demonstrated that conducting quantitative analysis using the GUI is straightforward and requires no coding. The results aligned with prior research and also offered potential insights into drug-related matters. Our hypothesis received partial confirmation because the final users were not involved in the evaluation process. Further studies, concentrating on ergonomics and the impact on professionals within regulatory agencies, are imperative for future research endeavors. We emphasized the versatility of our approach and the seamless interoperability between different modules over the performance of individual modules. Specifically, the annotation module was integrated early in the development process and could undergo substantial enhancement by leveraging contemporary techniques rooted in the Transformers architecture. Our pipeline holds potential applications in health surveillance by regulatory agencies or pharmaceutical companies, aiding in the identification of safety concerns. Moreover, it could be used by research teams for retrospective analysis of events.
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Affiliation(s)
- Pierre Karapetiantz
- Inserm, Sorbonne Université, université Paris 13, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé, LIMICS, F-75006, Paris, France
| | - Bissan Audeh
- Inserm, Sorbonne Université, université Paris 13, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé, LIMICS, F-75006, Paris, France
| | - Akram Redjdal
- Inserm, Sorbonne Université, université Paris 13, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé, LIMICS, F-75006, Paris, France
| | - Théophile Tiffet
- Service de santé publique et information médicale, CHU de Saint Etienne, 42000 Saint-Etienne, France
- Institut National de la Santé et de la Recherche Médicale, Université Jean Monnet, SAnté INgéniérie BIOlogie St-Etienne, SAINBIOSE, 42270 Saint-Priest-en-Jarez, France
| | - Cédric Bousquet
- Inserm, Sorbonne Université, université Paris 13, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé, LIMICS, F-75006, Paris, France
- Service de santé publique et information médicale, CHU de Saint Etienne, 42000 Saint-Etienne, France
| | - Marie-Christine Jaulent
- Inserm, Sorbonne Université, université Paris 13, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé, LIMICS, F-75006, Paris, France
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Gao P, Han H. Robust Web Data Extraction Based on Weighted Path-layer Similarity. JOURNAL OF COMPUTER INFORMATION SYSTEMS 2021. [DOI: 10.1080/08874417.2020.1861571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Peng Gao
- Laboratory of Information Security and Software Engineering, NARI Group Corporation/State Grid Electric Power Research Institute, Nanjing, China
| | - Hao Han
- Konica Minolta, Tokyo, Japan
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Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med Project. Drug Saf 2020; 43:835-851. [PMID: 32557179 DOI: 10.1007/s40264-020-00951-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
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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Karapetiantz P, Lillo-Le Louët A, Bousquet C. Informativité des forums de discussion français pour l’évaluation des effets indésirables du baclofène. Therapie 2019; 74:569-578. [DOI: 10.1016/j.therap.2019.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Revised: 02/26/2019] [Accepted: 05/23/2019] [Indexed: 10/26/2022]
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Audeh B, Calvier FE, Bellet F, Beyens MN, Pariente A, Lillo-Le Louet A, Bousquet C. Pharmacology and social media: Potentials and biases of web forums for drug mention analysis-case study of France. Health Informatics J 2019; 26:1253-1272. [PMID: 31566468 DOI: 10.1177/1460458219865128] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
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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Affiliation(s)
- Bissan Audeh
- Sorbonne Université and Université Paris 13, France
| | | | | | | | | | | | - Cedric Bousquet
- Sorbonne Université and Université Paris 13, France; CHU University Hospital of Saint-Etienne, France
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6
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Natsiavas P, Malousi A, Bousquet C, Jaulent MC, Koutkias V. Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches. Front Pharmacol 2019; 10:415. [PMID: 31156424 PMCID: PMC6533857 DOI: 10.3389/fphar.2019.00415] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Accepted: 04/02/2019] [Indexed: 12/12/2022] Open
Abstract
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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Affiliation(s)
- Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.,Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France
| | - Andigoni Malousi
- Laboratory of Biological Chemistry, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Cédric Bousquet
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France.,Public Health and Medical Information Unit, University Hospital of Saint-Etienne, Saint-Étienne, France
| | - Marie-Christine Jaulent
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France
| | - Vassilis Koutkias
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
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Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2019. [DOI: 10.1007/s41060-019-00175-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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8
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Lardon J, Bellet F, Aboukhamis R, Asfari H, Souvignet J, Jaulent MC, Beyens MN, Lillo-LeLouët A, Bousquet C. Evaluating Twitter as a complementary data source for pharmacovigilance. Expert Opin Drug Saf 2018; 17:763-774. [DOI: 10.1080/14740338.2018.1499724] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Jérémy Lardon
- Sorbonne Université, UPMC Université Paris 06, UMR_S 1142, LIMICS, Paris, France
- INSERM, U1142, LIMICS, Paris, France
- Université Paris 13, Sorbonne Paris Cité, LIMICS (UMR_S 1142), Bobigny, France
- Department of Public Health and medical informatics, CHU University of Saint-Etienne, Saint-Etienne, France
| | - Florelle Bellet
- Centre de Pharmacovigilance, Centre Hospitalier Universitaire (CHU) University Hospital of Saint-Etienne, Saint-Etienne, France
| | - Rim Aboukhamis
- Centre Régional de Pharmacovigilance, Hôpital Européen Georges Pompidou – Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Hadyl Asfari
- Sorbonne Université, UPMC Université Paris 06, UMR_S 1142, LIMICS, Paris, France
- INSERM, U1142, LIMICS, Paris, France
| | - Julien Souvignet
- Sorbonne Université, UPMC Université Paris 06, UMR_S 1142, LIMICS, Paris, France
- INSERM, U1142, LIMICS, Paris, France
- Université Paris 13, Sorbonne Paris Cité, LIMICS (UMR_S 1142), Bobigny, France
- Department of Public Health and medical informatics, CHU University of Saint-Etienne, Saint-Etienne, France
| | - Marie-Christine Jaulent
- Sorbonne Université, UPMC Université Paris 06, UMR_S 1142, LIMICS, Paris, France
- INSERM, U1142, LIMICS, Paris, France
- Université Paris 13, Sorbonne Paris Cité, LIMICS (UMR_S 1142), Bobigny, France
| | - Marie-Noëlle Beyens
- Centre de Pharmacovigilance, Centre Hospitalier Universitaire (CHU) University Hospital of Saint-Etienne, Saint-Etienne, France
| | - Agnès Lillo-LeLouët
- Centre Régional de Pharmacovigilance, Hôpital Européen Georges Pompidou – Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Cédric Bousquet
- Sorbonne Université, UPMC Université Paris 06, UMR_S 1142, LIMICS, Paris, France
- INSERM, U1142, LIMICS, Paris, France
- Université Paris 13, Sorbonne Paris Cité, LIMICS (UMR_S 1142), Bobigny, France
- Department of Public Health and medical informatics, CHU University of Saint-Etienne, Saint-Etienne, France
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Lopez-Aparicio S, Grythe H, Vogt M, Pierce M, Vallejo I. Webcrawling and machine learning as a new approach for the spatial distribution of atmospheric emissions. PLoS One 2018; 13:e0200650. [PMID: 30011313 PMCID: PMC6047804 DOI: 10.1371/journal.pone.0200650] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 06/30/2018] [Indexed: 11/19/2022] Open
Abstract
In this study we apply two methods for data collection that are relatively new in the field of atmospheric science. The two developed methods are designed to collect essential geo-localized information to be used as input data for a high resolution emission inventory for residential wood combustion (RWC). The first method is a webcrawler that extracts openly online available real estate data in a systematic way, and thereafter structures them for analysis. The webcrawler reads online Norwegian real estate advertisements and it collects the geo-position of the dwellings. Dwellings are classified according to the type (e.g., apartment, detached house) they belong to and the heating systems they are equipped with. The second method is a model trained for image recognition and classification based on machine learning techniques. The images from the real estate advertisements are collected and processed to identify wood burning installations, which are automatically classified according to the three classes used in official statistics, i.e., open fireplaces, stoves produced before 1998 and stoves produced after 1998. The model recognizes and classifies the wood appliances with a precision of 81%, 85% and 91% for open fireplaces, old stoves and new stoves, respectively. Emission factors are heavily dependent on technology and this information is therefore essential for determining accurate emissions. The collected data are compared with existing information from the statistical register at county and national level in Norway. The comparison shows good agreement for the proportion of residential heating systems between the webcrawled data and the official statistics. The high resolution and level of detail of the extracted data show the value of open data to improve emission inventories. With the increased amount and availability of data, the techniques presented here add significant value to emission accuracy and potential applications should also be considered across all emission sectors.
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Affiliation(s)
| | - Henrik Grythe
- NILU - Norwegian Institute for Air Research, Kjeller, Norway
| | - Matthias Vogt
- NILU - Norwegian Institute for Air Research, Kjeller, Norway
| | | | - Islen Vallejo
- NILU - Norwegian Institute for Air Research, Kjeller, Norway
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Karapetiantz P, Bellet F, Audeh B, Lardon J, Leprovost D, Aboukhamis R, Morlane-Hondère F, Grouin C, Burgun A, Katsahian S, Jaulent MC, Beyens MN, Lillo-Le Louët A, Bousquet C. Descriptions of Adverse Drug Reactions Are Less Informative in Forums Than in the French Pharmacovigilance Database but Provide More Unexpected Reactions. Front Pharmacol 2018; 9:439. [PMID: 29765326 PMCID: PMC5938397 DOI: 10.3389/fphar.2018.00439] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 04/13/2018] [Indexed: 01/28/2023] Open
Abstract
Background: 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. Methods: 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). Results: 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. Discussion: 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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Affiliation(s)
- Pierre Karapetiantz
- Sorbonne Université, INSERM, Université Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, Paris, France
| | - Florelle Bellet
- Centre Régional de Pharmacovigilance, Centre Hospitalier Universitaire de Saint-Étienne, Hôpital Nord, Saint-Étienne, France
| | - Bissan Audeh
- Université de Lyon, IMT Mines Saint-Etienne, Institut Henri Fayol, Département ISI, Université Jean Monnet, Institut d'Optique Graduate School, Centre National de la Recherche Scientifique, Laboratoire Hubert Curien, Saint-Étienne, France
| | - Jérémy Lardon
- Sorbonne Université, INSERM, Université Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, Paris, France
| | - Damien Leprovost
- Sorbonne Université, INSERM, Université Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, Paris, France
| | - Rim Aboukhamis
- Centre Régional de Pharmacovigilance, Hôpital Européen Georges-Pompidou, Assistance Publique - Hôpitaux de Paris, Paris, France
| | | | - Cyril Grouin
- LIMSI, CNRS, Université Paris-Saclay, Orsay, France
| | - Anita Burgun
- INSERM UMRS1138 Centre de Recherche des Cordeliers, Paris, France.,Département d'Informatique Médicale, Hôpital Européen Georges-Pompidou, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Sandrine Katsahian
- INSERM UMRS1138 Centre de Recherche des Cordeliers, Paris, France.,Département d'Informatique Médicale, Hôpital Européen Georges-Pompidou, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Marie-Christine Jaulent
- Sorbonne Université, INSERM, Université Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, Paris, France
| | - Marie-Noëlle Beyens
- Centre Régional de Pharmacovigilance, Centre Hospitalier Universitaire de Saint-Étienne, Hôpital Nord, Saint-Étienne, France
| | - Agnès Lillo-Le Louët
- Centre Régional de Pharmacovigilance, Hôpital Européen Georges-Pompidou, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Cédric Bousquet
- Sorbonne Université, INSERM, Université Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé, Paris, France
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