Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs

Yahya, Anwar Ali; Asiri, Yousef; Alyami, Ibrahim

doi:10.1155/2022/8965280

Cited by 3 publications

(6 citation statements)

References 65 publications

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“…Many of the publications state that similar patterns of adverse events were reported in social media as compared to other traditional pharmacovigilance data sources [35-43, 47, 48, 51-56, 60-62, 64-70, 74-78, 82, 83, 85-89, 92, 94, 98, 99, 102-105]. However, some studies [24,45,46,50,57,59,62,89,94,102,103] detected fewer numbers of adverse events on social media.…”

Section: Results Of Comparisonmentioning

confidence: 88%

“…A further seven studies [21, 24,45,50,55,56,81,95] used third party software to detect or extract ADE mentions. For 17 studies [48,51,58,64,65,69,70,75,80,82,83,85,94,97,98,[102][103][104][105]] some methodological issues were identified such as (1) lack of reproducibility [45], (2) no mention of manual validation of ADE mentions [58,85], (3) missing key information such as the volume of social media data from which the ADE signals were extracted or analysed [70][71][72], and (4) using lexical match for ADE detection or extraction [43,48,50,58,64,69,86,89,93,98]. For the remaining 29 studies [36-43, 46, 47, 49, attempted to remove non-personal accounts (such as organizations or companies).…”

Section: Methods Of Included Studiesmentioning

confidence: 99%

“…The most common method of comparing adverse events was by frequency (33 studies) [ [35,92,102,103] and recall [35], among other metrics such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) [56] of the detection were sometimes compared between different data sources to evaluate detection accuracy and specificity.…”

Section: Methods Of Comparisonmentioning

confidence: 99%

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The value of social media analysis for adverse events detection and pharmacovigilance: a Scoping Review (Preprint)

Golder,

O'Connor,

Wang

et al. 2024

JMIR Public Health and Surveillance

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Background: Adverse drug events pose a significant public health burden leading to hospitalisation, disability, and death. Even those adverse events categorised as non-serious can severely impact on patient quality of life, adherence, and persistence. Monitoring medication safety is challenging. Online patient reports on social media may be a useful supplementary source of realworld data. Despite the growth of sophisticated techniques for identifying adverse events using social media data, a consensus has not been reached as to the value of social media in relation to more traditional data sources.Objective: To evaluate and characterise the utility of social media analysis in adverse drug event detection and pharmacovigilance as compared to other data sources (such as spontaneous reporting systems and the clinical literature). Methods:In this scoping review, we searched 11 bibliographical databases and Google Scholar, followed by handsearching and forward and backward citation searching. Each records was screened by two independent reviewers at both title/abstract stage and the full-text screening stage. Studies were included if they 1) used any type of social media (such as Twitter or patient forums) to detect any adverse event associated with any drug medication and 2) compared the results ascertained from social media to any other data source. Study information was collated using a piloted data extraction sheet . Data were extracted on 1) the adverse events and drugs searched for and included, 2) the methods used (such as machine learning), 3) social media data source, 4) volume of data analysed, 5) limitations of the methodology, 6) availability of data and code, 7) comparison data source and comparison methods, 8) results, including the volume of adverse events, and how adverse events found compared to other data sources in their seriousness, frequencies, and expectedness/novelty (new vs. known knowledge) and 9) conclusions.Results: Of the 6538 unique records screened, 73 publications representing 60 studies and a wide variety of methods met our inclusion criteria. The most common social media platforms used were Twitter and online health forums. The most common comparator data source was spontaneous reporting systems, although other comparisons were made such as with scientific literature and product labels. Although similar patterns of adverse event reporting tended to be identified, the frequencies were lower in social media. Social media data were found useful in identifying new or unexpected adverse events and adverse events in a more timely manner.Conclusions: There is a large body of research comparing adverse events from social media to other sources. Most studies advocated the use of social media as an adjunct to traditional data sources. Some indicated the value of social media in understanding patient perspectives such as the impact of adverse events, which could be better explored.

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Section: Results Of Comparisonmentioning

confidence: 88%

Section: Methods Of Included Studiesmentioning

confidence: 99%

Section: Methods Of Comparisonmentioning

confidence: 99%

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The value of social media analysis for adverse events detection and pharmacovigilance: a Scoping Review (Preprint)

Golder,

O'Connor,

Wang

et al. 2024

JMIR Public Health and Surveillance

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confidence: 99%

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

mentioning

confidence: 99%