Adverse drug events (ADEs) in pediatric populations pose significant public health challenges, yet research on their detection and monitoring remains limited. This scoping review evaluates the use of unstructured data from electronic health records (EHRs) to identify ADEs in children. We searched six databases, including MEDLINE, Embase and IEEE Xplore, in September 2024. From 984 records, only nine studies met our inclusion criteria, indicating a significant gap in research towards identify ADEs in children. We found that unstructured data in EHRs can indeed be of value and enhance pediatric pharmacovigilance, although its use has been so far very limited. Traditional Natural Language Processing (NLP) methods have been employed to extract ADEs, but the approaches utilized face challenges in generalizability and context interpretation. These challenges could be addressed with recent advances in transformer-based models and large language models (LLMs), unlocking the use of EHR data at scale for pediatric pharmacovigilance.

The authors have declared no competing interest.

This work was supported by the National Institutes of Health (NIH) National Library of Medicine (NLM) under grant NIH-NLM R01LM011176. The NIH-NLM funded this research but was not involved in the design or conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.

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