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Related Experiment Video

Updated: Sep 28, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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[Artificial intelligence-based literature data warehouse for vaccine safety].

Y Yang1, X Y Zeng2, Z K Liu2

  • 1National Institute of Health Data Science, Peking University, Beijing 100191, China.

Zhonghua Liu Xing Bing Xue Za Zhi = Zhonghua Liuxingbingxue Zazhi
|March 29, 2022
PubMed
Summary

A new literature data warehouse supports global vaccine safety assessment. This AI-powered system efficiently screens and organizes vaccine safety research for evidence-based policy decisions.

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Area of Science:

  • Pharmacovigilance
  • Public Health
  • Information Science

Background:

  • Global vaccine safety assessment requires robust, up-to-date data.
  • Existing literature review processes can be time-consuming and resource-intensive.

Purpose of the Study:

  • To establish a sustainable, updated literature data warehouse for global vaccine safety assessment.
  • To provide data support for evidence-based vaccine safety evaluations.

Main Methods:

  • Semi-automated construction using AI and systematic review procedures.
  • Literature retrieval from 9 English and Chinese databases, including ClinicalTrials.gov.
  • Two-round screening (AI and manual) and classification by vaccine type and adverse events.

Main Results:

  • Over 0.41 million articles retrieved; 23,304 included after screening.
  • Demonstration studies completed on DPT vaccine and encephalopathy/encephalitis, allergic purpura, and brachial plexus neuritis.
  • Established an updating strategy for regular data warehouse maintenance.

Conclusions:

  • The data warehouse provides high-quality data for vaccine safety research.
  • Supports evidence-based immunization policy-making and methodological research.
  • Demonstration studies offer a framework for timely, efficient evidence-based vaccine safety assessment.