Can social media data lead to earlier detection of drug‐related adverse events?

Duh, Mei Sheng; Crémieux, Pierre‐Yves; Audenrode, Marc Van; Vekeman, Francis; Karner, Paul; Zhang, Haimin; Greenberg, Paul E.

doi:10.1002/pds.4090

Cited by 51 publications

(55 citation statements)

References 9 publications

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“…The qualitative exploratory analysis of the SDRs detected earlier showed that the events were related to serious as well as patient related symptoms. This finding are in light with recent studies [14] which addressed the question on earlier detection of drug-related adverse events in the social media as compared to FDA Adverse Event Reporting System (FAERS). The findings highlighted some of the promises of social media data sources for detecting early AE reports patterns compared to conventional pharmacovigilance sources and showed that social media AE reports helped predict the occurrence of FAERS reports several months later for one of the two drugs that were studied.…”

Section: Discussionsupporting

confidence: 52%

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

Kürzinger¹,

Schück²,

Texier³

et al. 2018

Preprint

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Background: While traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of webbased data relies on their volume and real time availability allowing early signals of disproportionate reporting (SDRs) detection.Objectives: This study aimed at assessing (1) the consistency of SDRs detected from patient's medical forums in France compared to traditional reporting systems and (2) the ability of identifying SDRs earlier than the traditional reporting systems. Methods:Messages posted on patients' forums between 2005 and 2015 were used. Eight disproportionality definitions were retained. Comparison of SDRs from the forums to SDRs detected in VigiBase® was done by describing the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, the Receiver Operating Characteristics curve and the area under the curve (AUC). Time difference in months between the detection dates of SDRs from the forums and from VigiBase® was provided. Results: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 EBGM. Up to 37.5% of the SDRs were detected earlier in the forums than in VigiBase®. Conclusion: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 37.5% of the SDRs could have been detected earlier, thus facilitating increased safety of the 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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Section: Discussionsupporting

confidence: 52%

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

Kürzinger¹,

Schück²,

Texier³

et al. 2018

Preprint

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“…The examination of SM utility in identifying AEs and SDRs has been previously performed by multiple groups, using various approaches. Some authors have concluded that SM should be further considered as a potential SD tool, while others concluded that SM shows a poor performance in AE and SDR identification . Although we count this work among the former group, we do not disagree with the core conclusions of the latter.…”

Section: Discussionmentioning

confidence: 52%

“…Some authors have concluded that SM should be further considered as a potential SD tool, 17,18 while others concluded that SM shows a poor performance in AE and SDR identification. 19,20 Although we count this work among the former group, we do not disagree with the core conclusions of the latter. Namely, we agree that SM cannot be used as a stand-alone data source and should not replace other pharmacovigilance activities.…”

Section: Discussionmentioning

confidence: 56%

Comparison of text processing methods in social media–based signal detection

Gavrielov‐Yusim¹,

Kürzinger

Nishikawa

et al. 2019

Pharmacoepidemiology and Drug

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Purpose Adverse event (AE) identification in social media (SM) can be performed using various types of natural language processing (NLP) and machine learning (ML). These methods can be categorized by complexity and precision level. Co‐occurrence‐based ML methods are rather basic, as they identify simultaneous appearance of drugs and clinical events in a single post. In contrast, statistical learning methods involve more complex NLP and identify drugs, events, and associations between them. We aimed to compare the ability of co‐occurrence and NLP to identify AEs and signals of disproportionate reporting (SDR) in patient‐generated SM. We also examined the performance of lift in SM‐based signal detection (SD). Methods Our examination was performed in a corpus of SM posts crawled from open online patient forums and communities, using the spontaneously reported VigiBase data as reference data set. Results We found that co‐occurrence and NLP produce AEs, which are 57% and 93% consistent with VigiBase AEs, respectively. Among the SDRs identified both in SM and in VigiBase, up to 55.3% were identified earlier in co‐occurrence, and up to 32.1% were identified earlier in NLP‐processed SM. Using lift in SM SD provided performance similar to frequentist methods, both in co‐occurrence and in NLP‐processed AEs. Conclusion Our results indicate that using SM as a data source complementary to traditional pharmacovigilance sources should be considered further. Various levels of SM processing may be considered, depending on the preferred policies and tolerance for false‐positive to false‐negative balance in routine pharmacovigilance processes.

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“…Social media generate patient-centric data, which is typically unfiltered and unchecked, and can use the incorrect terms, or refer to diagnoses that are based on Internet research rather than confirmed diagnoses from healthcare professionals (risk of misinformation). Social media disclosures are also conditioned by the disclosers gender and cultural background, resulting in differences in linguistic [91,157]. Hence, pharmacovigilance practice could benefit from empowered, motivated and knowledgeable patients, through increased public awareness of pharmacovigilance, through the closer engagement of pharmacovigilance stakeholders (regulatory authorities, industry) with patients, through the diffusion of scientific knowledge regarding health, illness, therapy and medicines, and through the further strengthening of health support networks and communities.…”

Section: Value/utility Of Social Media As Knowledge Sources For Pharmmentioning

confidence: 99%

Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions

Pappa

Stergioulas

2019

Int J Data Sci Anal

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The ever-increasing supply of information combined with the growing knowledge elicitation capabilities of key emerging technologies presents pharmacovigilance with enormous opportunities. Currently, safety monitoring is expanding its evidence base, moving beyond traditional approaches towards sophisticated methods that can identify possible safety signals from multiple information sources, both structured and unstructured. In this context, health information posted online by patients represents a potentially valuable, yet currently left largely unexploited source of post-market safety data that could supplement data from traditional sources of drug safety information. As the use of social media data for pharmacovigilance is still in its infancy, the present paper explores the state of the art in the application of social data to adverse drug reaction detection; provides a thorough review of existing work in the field, highlighting important research efforts and achievements; and finally, discusses the current challenges and promising avenues for future work. Following a literature review methodology, a critical appraisal was conducted of carefully selected work on the use of social data in post-market surveillance, as presented in the recent scientific literature. Out of a sample of more than 1300 articles, which was the result of the literature search, the final selection of articles was made based on their relevance to the applications of social networking sites (SNS) to pharmacovigilance, and a thorough review of this corpus was completed with a total of 100 articles reviewed. The main contributions of this review include the mapping and systematisation of the current knowledge in the field by drawing comparisons of different approaches, types of social data and of relevant sources currently used in the field, and by developing new classifications of social data sources and taxonomies for social data for use in pharmacovigilance, as well as the identification of key challenges and the extraction of new insights in terms of potential for practical applications and future research directions in the area of pharmacovigilance. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Can social media data lead to earlier detection of drug‐related adverse events?

Cited by 51 publications

References 9 publications

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

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

Comparison of text processing methods in social media–based signal detection

Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions

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