ArticleJMIR research protocols2017
The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process.
Article in JMIR research protocols, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Crowdsourcing Adverse Events Associated With Monoclonal Antibodies Targeting Calcitonin Gene-Related Peptide Signaling for Migraine Prevention: Natural Language Processing Analysis of Social Media.JMIR formative research · 2024Article
- Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study.Journal of medical Internet research · 2024Article
- Adverse Event Signal Detection Using Patients' Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models.Journal of medical Internet research · 2024Article
- [Research Progress of Antitumor Pharmacovigilance].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2022Article
- Automated gathering of real-world data from online patient forums can complement pharmacovigilance for rare cancers.Scientific reports · 2022Article
- [Utilizing social media data in post-market safety surveillance].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2021Article
- Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med Project.Drug safety · 2020Article
- Systematic Evaluation of Research Progress on Natural Language Processing in Medicine Over the Past 20 Years: Bibliometric Study on PubMed.Journal of medical Internet research · 2020Review
- The Adverse Drug Reactions From Patient Reports in Social Media Project: Protocol for an Evaluation Against a Gold Standard.JMIR research protocols · 2019Article
- Pruritus after discontinuation of cetirizine.Therapeutic advances in drug safety · 2019Article
- Using social media in safety signal management: is it reliable?Therapeutic advances in drug safety · 2018Review
- Overview of this issue: pharmacovigilance, what is new?International journal of clinical pharmacy · 2018Article
- Pharmacovigilance in oncology.International journal of clinical pharmacy · 2018Review
- Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach.Journal of medical Internet research · 2018Article
- Training Augmented Intelligent Capabilities for Pharmacovigilance: Applying Deep-learning Approaches to Individual Case Safety Report Processing.Pharmaceutical medicine · 2018Article
- Mining Patients' Narratives in Social Media for Pharmacovigilance: Adverse Effects and Misuse of Methylphenidate.Frontiers in pharmacology · 2018Article
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Authors and funding
14 authors.
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Abstract
backgroundAdverse 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.
objectiveThis 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.
methodsIn 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.
resultsConsidering 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.
conclusionsWe 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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