Adverse event detection by integrating twitter data and VAERS

• Published in: Journal of biomedical semantics Vol. 9; no. 1; p. 19
• Main Authors: Wang, Junxiang, Zhao, Liang, Ye, Yanfang, Zhang, Yuji
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20-06-2018; BioMed Central; BMC
• Subjects: Case studies; Classification; Combinatorial analysis; Complications and side effects; Data mining; Detectors; Digital media; Disease; Drug therapy, Combination; Formal reports; Immunization; Influenza; Influenza vaccines; Information retrieval; Information systems; International conferences; Labelling; Labels; Learning; Media; Multi-instance learning; Performance enhancement; Performance measurement; Public health; Social media; Social networks; Swine flu; Teaching methods; Training; Vaccine adverse event detection; Vaccines; New York; United States > US; Formal reports; Multi-instance learning; Social media; Vaccine adverse event detection
• ISSN: 2041-1480; 2041-1480
• DOI: 10.1186/s13326-018-0184-y

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.