Machine learning-assisted mRNA vaccine pharmacovigilance: a systematic review of multi-source real-world data

BMC Medical Informatics and Decision Making

卷:26

Systematic Review

YuLong He¹  Yan Mao¹  XinYu Wang¹ 

[1] Department of Clinical Medicine, Hunan University of Traditional Chinese Medicine, 300 Xueshi Road, Hanpu Science and Education Park, Yuelu District, 410208, Changsha, Hunan, China

关键词: Machine learning;    mRNA vaccines;    Artificial intelligence;    Pharmacovigilance;    Multi-source data integration;    Adverse event detection;    Myocarditis;    Deep learning;    Natural language processing;   

DOI  :  10.1186/s12911-026-03832-3

 received in 2026-06-18, accepted in 2026-09-03,  发布年份 2026

来源: Springer

【 摘 要 】

BackgroundThe rapid deployment of mRNA vaccines during the COVID-19 pandemic exposed limitations in traditional pharmacovigilance systems, including delayed reporting, high underreporting rates, and inability to calculate true incidence. Machine learning (ML) offers new pathways to overcome these challenges by integrating multi-source real-world data.MethodsWe systematically reviewed English-language studies from database inception to June 2026. Searches were performed in PubMed, Embase, and Web of Science. Two reviewers independently screened records. Given substantial heterogeneity across ML tasks (signal detection, text extraction, risk prediction, prognosis stratification), algorithms, data sources, and metrics, we performed narrative synthesis. Risk of bias was assessed using adapted QUADAS-2.ResultsWe identified 43 studies. For adverse-event prediction, tree-based models reported AUCs of 0.85–0.87, though estimates derive from heterogeneous settings. NLP reduced redundant signals by 17% in vaccine reporting systems. For myocarditis, ML models reached AUCs up to 0.899 in cardiovascular cohorts, but direct mRNA vaccine applications remain limited and retrospective. Emerging platforms (self-amplifying and tumor mRNA vaccines) lack post-marketing data, rendering ML applications largely conceptual.ConclusionML-assisted pharmacovigilance enables a shift from passive to active, intelligent monitoring. Despite challenges in data quality, model interpretability, and regulatory approval, intelligent pharmacovigilance systems will become essential infrastructure for safeguarding public health.

【 授权许可】

CC BY   
© The Author(s) 2026

【 图 表 】

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