Challenge
Collaborators at the University of Texas School of Biomedical Informatics, in conjunction with members of the Melax Tech team, recently published work demonstrating the feasibility of using AI-based approaches incorporating deep learning to facilitate automated and accurate information extraction from post-marketing vaccine safety reports.
The US Vaccine Adverse Event Reporting System (VAERS) collects data on possible problems with vaccines in conjunction with the CDC and the FDA. It is a major resource for those studying the incidence and possible causes of adverse events post-vaccination. As of 2020, the system contained over 650,000 reports submitted by healthcare providers, vaccine manufacturers, and the public. While VAERS contains structured data, much of the detailed data is in unstructured text. Given this data's size, scale, and rate of growth, automated analysis of these reports is important to allow researchers and physicians to understand the progression of rare but severe vaccine adverse events.
Results
In our team’s work, we focused on the Guillain-Barré syndrome (GBS) occurrence post-influenza vaccination. We implemented and evaluated a variety of state-of-the-art deep learning algorithms for named entity recognition (NER) from the GBS-related influenza vaccine safety reports. Few other machine learning-based studies have been reported on adverse event extraction from VAERS. Melax Tech’s AI-based NLP system, CLAMP, was used for this project's model development and NLP work. This work should interest anyone attempting to review and analyze adverse event reports for new and existing vaccines, including immunology researchers and the pharmaceutical industry.
Our methodology and results are described in “Extracting post-marketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning.” Jingcheng Du, Yang Xiang, et al. Journal of the American Medical Informatics Association, Volume 28, Issue 7, July 2021, Pages 1393–1400,https://doi.org/10.1093/jamia/ocab014