Dataset Information

Adverse Events in Twitter-Development of a Benchmark Reference Dataset: Results from IMI WEB-RADR.

ABSTRACT: INTRODUCTION AND OBJECTIVE: Social media has been suggested as a source for safety information, supplementing existing safety surveillance data sources. This article summarises the activities undertaken, and the associated challenges, to create a benchmark reference dataset that can be used to evaluate the performance of automated methods and systems for adverse event recognition. METHODS:A retrospective analysis of public English-language Twitter posts (Tweets) was performed. We sampled 57,473 Tweets out of 5,645,336 Tweets created between 1 March, 2012 and 1 March, 2015 that mentioned at least one of six medicinal products of interest (insulin glargine, levetiracetam, methylphenidate, sorafenib, terbinafine, zolpidem). Products, adverse events, indications, product-event combinations, and product-indication combinations were extracted and coded by two independent teams of safety reviewers. RESULTS:The benchmark reference dataset consisted of 1056 positive controls ("adverse event Tweets") and 56,417 negative controls ("non-adverse event Tweets"). The 1056 adverse event Tweets contained 1396 product-event combinations referring to personal adverse event experiences, comprising 292 different MedDRA® Preferred Terms. The 1171 product-event combinations (83.9%) were confined to four MedDRA® System Organ Classes. The 195 Tweets (18.5%) contained indication information, comprising 25 different Preferred Terms. CONCLUSIONS:A manually curated benchmark reference dataset based on Twitter data has been created and is made available to the research community to evaluate the performance of automated methods and systems for adverse event recognition in unstructured free-text information.

SUBMITTER: Dietrich J

PROVIDER: S-EPMC7165158 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Adverse Events in Twitter-Development of a Benchmark Reference Dataset: Results from IMI WEB-RADR.

Dietrich Juergen J Gattepaille Lucie M LM Grum Britta Anne BA Jiri Letitia L Lerch Magnus M Sartori Daniele D Wisniewski Antoni A

Drug safety 20200501 5

INTRODUCTION AND OBJECTIVE: Social media has been suggested as a source for safety information, supplementing existing safety surveillance data sources. This article summarises the activities undertaken, and the associated challenges, to create a benchmark reference dataset that can be used to evaluate the performance of automated methods and systems for adverse event recognition.<h4>Methods</h4>A retrospective analysis of public English-language Twitter posts (Tweets) was performed. We sampled ...[more]

PMID: 31997289

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Project description:INTRODUCTION:A large number of studies on systems to detect and sometimes normalize adverse events (AEs) in social media have been published, but evidence of their practical utility is scarce. This raises the question of the transferability of such systems to new settings. OBJECTIVES:The aims of this study were to develop an AE recognition system, prospectively evaluate its performance on an external benchmark dataset and identify potential factors influencing the transferability of AE recognition systems. METHODS:A pipeline based on dictionary lookups and logistic regression classifiers was developed using a proprietary dataset of 196,533 Tweets manually annotated for AE relations and prospectively evaluated the system on the publicly available WEB-RADR reference dataset, exploring different aspects affecting transferability. RESULTS:Our system achieved 0.53 precision, 0.52 recall and 0.52 F1-score on the development test set; however, when applied to the WEB-RADR reference dataset, system performance dropped to 0.38 precision, 0.20 recall and 0.26 F1-score. Similarly, a previously published method aiming at automatically detecting adverse event posts reported 0.5 precision, 0.92 recall and 0.65 F1-score on thus another dataset, while performance on the WEB-RADR reference dataset was reduced to 0.37 precision, 0.63 recall and 0.46 F1-score. We identified four potential factors leading to poor transferability: overfitting, selection bias, label bias and prevalence. CONCLUSION:We warn the community about a potentially large discrepancy between the expected performance of automated AE recognition systems based on published results and the actual observed performance on independent data. This study highlights the difficulty of implementing an all-purpose system for automatic adverse event recognition in Twitter, which could explain the lack of such systems in practical pharmacovigilance settings. Our recommendation is to use benchmark independent datasets, such as the WEB-RADR reference, to investigate the transferability of the adverse event recognition systems and ultimately enforce rigorous comparisons across studies on the task.

Dataset Information

Adverse Events in Twitter-Development of a Benchmark Reference Dataset: Results from IMI WEB-RADR.

Publications

Adverse Events in Twitter-Development of a Benchmark Reference Dataset: Results from IMI WEB-RADR.

Similar Datasets

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