ArticleAnnual review of biomedical data science2025
Leveraging Unstructured Data in Electronic Health Records to Detect Adverse Events from Pediatric Drug Use: A Scoping Review.
Article in Annual review of biomedical data science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Computable longitudinal patient journeys from structured and unstructured EHR data.Nature medicine · 2026Article
- Comment on: Primary suspect drugs of cataracts in pediatric patients: FDA adverse events reporting database analysis.Journal of cataract and refractive surgery · 2026Article
- Article
- Large Language Models for Adverse Drug Events: A Clinical Perspective.Journal of clinical medicine · 2025Review
Corrections and comments
- Update of
Authors and funding
5 authors.
Funding
Abstract
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 toward identifying ADEs in children. We found that unstructured data in EHRs can indeed be of value and enhance pediatric pharmacovigilance, although their use has been so far very limited. Traditional natural language processing 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, unlocking the use of EHR data at scale for pediatric pharmacovigilance.
Indexed as
Identifiers
40789734What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.