ArticlePLOS digital health2023
BERT based natural language processing for triage of adverse drug reaction reports shows close to human-level performance.
Article in PLOS digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed, 17 citations in OpenAlex.
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- How is AI developing in pharmacovigilance?Therapeutic advances in drug safety · 2026Article
- Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study.JMIR formative research · 2025Article
- Waldo: Automated discovery of adverse events from unstructured self reports.PLOS digital health · 2025Article
- Predicting Drug-Side Effect Relationships From Parametric Knowledge Embedded in Biomedical BERT Models: Methodological Study With a Natural Language Processing Approach.JMIR medical informatics · 2025Article
- Quality of Clinical Information in Pregnancy Pharmacovigilance Data Sources-A Contribution of the ConcePTION Project.Pharmacoepidemiology and drug safety · 2025Article
- Orchestrating generative AI in pharmacovigilance: predicting and preempting the unpredictable.Therapeutic advances in drug safety · 2025Review
- A national pharmacovigilance centre perspective on pandemic preparedness -Frontiers in drug safety and regulation · 2025Article
- An explainable machine learning model for predicting chronic coronary disease and identifying valuable text features.Frontiers in cardiovascular medicine · 2025Article
- Year 2023 in Biomedical Natural Language Processing: a Tribute to Large Language Models and Generative AI.Yearbook of medical informatics · 2024Article
- Editorial: Traditional and innovative approaches for signal detection.Frontiers in drug safety and regulation · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Post-marketing reports of suspected adverse drug reactions are important for establishing the safety profile of a medicinal product. However, a high influx of reports poses a challenge for regulatory authorities as a delay in identification of previously unknown adverse drug reactions can potentially be harmful to patients. In this study, we use natural language processing (NLP) to predict whether a report is of serious nature based solely on the free-text fields and adverse event terms in the report, potentially allowing reports mislabelled at time of reporting to be detected and prioritized for assessment. We consider four different NLP models at various levels of complexity, bootstrap their train-validation data split to eliminate random effects in the performance estimates and conduct prospective testing to avoid the risk of data leakage. Using a Swedish BERT based language model, continued language pre-training and final classification training, we achieve close to human-level performance in this task. Model architectures based on less complex technical foundation such as bag-of-words approaches and LSTM neural networks trained with random initiation of weights appear to perform less well, likely due to the lack of robustness that a base of general language training provides.
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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.