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Adverse event detection by integrating twitter data and VAERS

Overview of attention for article published in Journal of Biomedical Semantics, June 2018
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Title
Adverse event detection by integrating twitter data and VAERS
Published in
Journal of Biomedical Semantics, June 2018
DOI 10.1186/s13326-018-0184-y
Pubmed ID
Authors

Junxiang Wang, Liang Zhao, Yanfang Ye, Yuji Zhang

Abstract

Vaccine has been one of the most successful public health interventions to date. However, vaccines are pharmaceutical products that carry risks so that many adverse events (AEs) are reported after receiving vaccines. Traditional adverse event reporting systems suffer from several crucial challenges including poor timeliness. This motivates increasing social media-based detection systems, which demonstrate successful capability to capture timely and prevalent disease information. Despite these advantages, social media-based AE detection suffers from serious challenges such as labor-intensive labeling and class imbalance of the training data. To tackle both challenges from traditional reporting systems and social media, we exploit their complementary strength and develop a combinatorial classification approach by integrating Twitter data and the Vaccine Adverse Event Reporting System (VAERS) information aiming to identify potential AEs after influenza vaccine. Specifically, we combine formal reports which have accurately predefined labels with social media data to reduce the cost of manual labeling; in order to combat the class imbalance problem, a max-rule based multi-instance learning method is proposed to bias positive users. Various experiments were conducted to validate our model compared with other baselines. We observed that (1) multi-instance learning methods outperformed baselines when only Twitter data were used; (2) formal reports helped improve the performance metrics of our multi-instance learning methods consistently while affecting the performance of other baselines negatively; (3) the effect of formal reports was more obvious when the training size was smaller. Case studies show that our model labeled users and tweets accurately. We have developed a framework to detect vaccine AEs by combining formal reports with social media data. We demonstrate the power of formal reports on the performance improvement of AE detection when the amount of social media data was small. Various experiments and case studies show the effectiveness of our model.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 14%
Student > Master 10 14%
Other 8 11%
Student > Bachelor 8 11%
Student > Doctoral Student 4 5%
Other 13 18%
Unknown 20 27%
Readers by discipline Count As %
Computer Science 14 19%
Medicine and Dentistry 9 12%
Nursing and Health Professions 5 7%
Biochemistry, Genetics and Molecular Biology 3 4%
Business, Management and Accounting 2 3%
Other 15 21%
Unknown 25 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 July 2021.
All research outputs
#14,851,073
of 25,473,687 outputs
Outputs from Journal of Biomedical Semantics
#190
of 368 outputs
Outputs of similar age
#173,883
of 341,748 outputs
Outputs of similar age from Journal of Biomedical Semantics
#3
of 4 outputs
Altmetric has tracked 25,473,687 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 368 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 341,748 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.