# Dynamic learning for post-vaccine event prediction using temporal information in VAERS

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2020 · $766,226

## Abstract

Project Summary
Vaccines have been one of the most successful public health interventions to date. They are, however,
pharmaceutical products that carry risks. Effective analyses of post-vaccination adverse events (AEs) is vital to
assuring the safety of vaccines, a key public health intervention for reducing the frequency of vaccine-
preventable illnesses. The CDC/FDA Vaccine Adverse Event Reporting System (VAERS) contains up to
30,000 reports per year over the past 25 years. VAERS reports include both structured data (e.g., vaccination
date, first onset date, age, and gender) and unstructured narratives that often provide detailed clinical
information about the clinical events and the temporal relationship of the series of event occurrences post
vaccination. The structured data only provide one onsite date whereas temporal information of the sequence of
events post vaccination is contained in the unstructured narratives. Current status –While structured data in
the VAERS are widely used, the narratives are generally ignored because of the challenges inherent in
working with unstructured data. Without these narratives, potentially valuable information is lost. Goals - In
response to the FOA, PA-15-312, this proposed project focuses on the specific objective on
“creation/evaluation of statistical methodologies for analyzing data on vaccine safety, including data available
from existing data sources such as passive reporting systems or healthcare databases”. We propose to
develop a novel framework to extract and accurately interpret the temporal information contained in the
narratives through informatics approaches, and to develop prediction models for risk of severe AEs.
Specifically, built upon the state-of-art ontology and natural language processing technologies, we will develop
and validate a Temporal Information Modeling, Extraction and Reasoning system for Vaccine data (TIMER-V),
which will automatically extract post-vaccination events and their temporal relationships from VAERS reports,
semantically infer temporal relations, and integrate the exacted unstructured data with the structured data.
Furthermore, we will provide and maintain a publicly available data access interface to query the new
integrated data repository, which will facilitate vaccine safety research, casual inference, and other temporal
related discovery. We will also develop and validate models to predict severe AEs using the co-occurrence or
temporal patterns of the series of AEs post vaccination. To the best of our knowledge, this is the first attempt to
make use of the unstructured narratives in the VAERS reports to facilitate the temporal related discovery to a
broad community of investigators in pharmacology, pharmacoepidemiology, vaccine safety research, among
others.

## Key facts

- **NIH application ID:** 9854882
- **Project number:** 5R01AI130460-04
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Yong Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $766,226
- **Award type:** 5
- **Project period:** 2017-02-01 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9854882

## Citation

> US National Institutes of Health, RePORTER application 9854882, Dynamic learning for post-vaccine event prediction using temporal information in VAERS (5R01AI130460-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9854882. Licensed CC0.

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