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      Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis

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          Abstract

          Background

          While traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of Web-based data relies on their volume and real-time availability, allowing early detection of signals of disproportionate reporting (SDRs).

          Objective

          This study aimed (1) to assess the consistency of SDRs detected from patients’ medical forums in France compared with those detected from the traditional reporting systems and (2) to assess the ability of SDRs in identifying earlier than the traditional reporting systems.

          Methods

          Messages posted on patients’ forums between 2005 and 2015 were used. We retained 8 disproportionality definitions. Comparison of SDRs from the forums with SDRs detected in VigiBase was done by describing the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, receiver operating characteristics curve, and the area under the curve (AUC). The time difference in months between the detection dates of SDRs from the forums and VigiBase was provided.

          Results

          The comparison analysis showed that the sensitivity ranged from 29% to 50.6%, the specificity from 86.1% to 95.5%, the PPV from 51.2% to 75.4%, the NPV from 68.5% to 91.6%, and the accuracy from 68% to 87.7%. The AUC reached 0.85 when using the metric empirical Bayes geometric mean. Up to 38% (12/32) of the SDRs were detected earlier in the forums than that in VigiBase.

          Conclusions

          The specificity, PPV, and NPV were high. The overall performance was good, showing that data from medical forums may be a valuable source for signal detection. In total, up to 38% (12/32) of the SDRs could have been detected earlier, thus, ensuring the increased safety of patients. Further enhancements are needed to investigate the reliability and validation of patients’ medical forums worldwide, the extension of this analysis to all possible drugs or at least to a wider selection of drugs, as well as to further assess performance against established signals.

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          Most cited references17

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          Utilizing social media data for pharmacovigilance: A review.

          Automatic monitoring of Adverse Drug Reactions (ADRs), defined as adverse patient outcomes caused by medications, is a challenging research problem that is currently receiving significant attention from the medical informatics community. In recent years, user-posted data on social media, primarily due to its sheer volume, has become a useful resource for ADR monitoring. Research using social media data has progressed using various data sources and techniques, making it difficult to compare distinct systems and their performances. In this paper, we perform a methodical review to characterize the different approaches to ADR detection/extraction from social media, and their applicability to pharmacovigilance. In addition, we present a potential systematic pathway to ADR monitoring from social media.
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            Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system.

            Signal-detection algorithms (SDAs) are recognized as vital tools in pharmacovigilance. However, their performance characteristics are generally unknown. By leveraging a unique gold standard recently made public by the Observational Medical Outcomes Partnership (OMOP) and by conducting a unique systematic evaluation, we provide new insights into the diagnostic potential and characteristics of SDAs that are routinely applied to the US Food and Drug Administration (FDA) Adverse Event Reporting System (AERS). We find that SDAs can attain reasonable predictive accuracy in signaling adverse events. Two performance classes emerge, indicating that the class of approaches that address confounding and masking effects benefits safety surveillance. Our study shows that not all events are equally detectable, suggesting that specific events might be monitored more effectively using other data sources. We provide performance guidelines for several operating scenarios to inform the trade-off between sensitivity and specificity for specific use cases. We also propose an approach and demonstrate its application in identifying optimal signaling thresholds, given specific misclassification tolerances.
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              Text mining for adverse drug events: the promise, challenges, and state of the art.

              Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event (ADE) detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources-such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs-that are amenable to text mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                November 2018
                20 November 2018
                : 20
                : 11
                : e10466
                Affiliations
                [1 ] Epidemiology and Benefit Risk Evaluation Sanofi Chilly-Mazarin France
                [2 ] Kappa Santé Paris France
                [3 ] Kap Code Paris France
                [4 ] Information Technology and Solutions Sanofi Lyon France
                [5 ] Global Pharmacovigilance Sanofi Bridgewater, NJ United States
                [6 ] Epidemiology and Benefit Risk Evaluation Sanofi Bridgewater, NJ United States
                Author notes
                Corresponding Author: Marie-Laure Kürzinger marie-laure.kurzinger@ 123456sanofi.com
                Author information
                http://orcid.org/0000-0002-2606-776X
                http://orcid.org/0000-0003-2642-7726
                http://orcid.org/0000-0003-3749-254X
                http://orcid.org/0000-0002-2938-7478
                http://orcid.org/0000-0002-1500-0236
                http://orcid.org/0000-0001-8630-0052
                http://orcid.org/0000-0002-1355-3793
                http://orcid.org/0000-0002-0312-9008
                http://orcid.org/0000-0002-5616-558X
                http://orcid.org/0000-0001-8235-8388
                Article
                v20i11e10466
                10.2196/10466
                6280030
                30459145
                43ba6824-f677-49ac-8620-5f3dee13a5fe
                ©Marie-Laure Kürzinger, Stéphane Schück, Nathalie Texier, Redhouane Abdellaoui, Carole Faviez, Julie Pouget, Ling Zhang, Stéphanie Tcherny-Lessenot, Stephen Lin, Juhaeri Juhaeri. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 20.11.2018.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/.as well as this copyright and license information must be included.

                History
                : 23 March 2018
                : 25 April 2018
                : 29 June 2018
                : 29 June 2018
                Categories
                Original Paper
                Original Paper

                Medicine
                adverse event,internet,medical forums,pharmacovigilance,signal detection,signals of disproportionate reporting,social media

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