# Incorporation of multilevel ontologies of adverse events and vaccines for vaccine safety surveillance

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $171,303

## Abstract

Incorporation of multilevel ontologies of adverse events and vaccines
 for vaccine safety surveillance
PROJECT SUMMARY
Vaccines face tougher safety standards than most pharmaceutical products because they are given
to healthy people, often children. Effective and rigorous analyses of post-vaccination adverse events
(AEs) is critical to ensure the safety of vaccines. The Vaccine Adverse Event Reporting System
(VAERS) is a national vaccine safety surveillance program which contains spontaneous reports from
1990 to present. Statistical approaches have been used on VAERS to extract important signals
hidden in this large, complex database and offer a hypothesis-free view of the safety characteristics in
the underlying data. However, existing methods may miss detecting serious AEs due to modeling
under the false assumption of independence between different types of AEs.
In response to the FOA, PA-18-873, this proposal addresses the specific objective:
“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 series of methods for vaccine safety surveillance while incorporating
adverse event ontology as well as vaccine ontology. Specifically, we will use the Medical Dictionary
for Regulatory Activities (MedDRA) and the vaccine ontology (VO) to form the basis of our models for
systematically mining and monitoring safety signals. To the best of our knowledge, this is the first
attempt to directly incorporate AE and vaccine ontologies in the signal detection method. Multiple AEs
may individually be rare enough to go undetected, but if they are related, they can borrow strength
from each other to increase the chance of being flagged. Furthermore, borrowing strength induces
shrinkage of related AEs, thereby also reducing headline-grabbing false positives. Additionally,
multiple AEs may collectively point to an underlying adverse cause, combined with additional expert
knowledge from the vaccine ontology, such as vaccine components, we will be able to understand the
root cause of different types of AEs.
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## Key facts

- **NIH application ID:** 10327740
- **Project number:** 5R01AI158543-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Lili Zhao
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $171,303
- **Award type:** 5
- **Project period:** 2021-01-11 → 2022-07-24

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10327740

## Citation

> US National Institutes of Health, RePORTER application 10327740, Incorporation of multilevel ontologies of adverse events and vaccines for vaccine safety surveillance (5R01AI158543-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10327740. Licensed CC0.

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