ArticleJAMIA open2024
Development of a text mining algorithm for identifying adverse drug reactions in electronic health records.
Article in JAMIA open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Using Natural Language Processing to Identify Adverse Drug Events Characterized by Medication Replacement in Primary Care Electronic Medical Records: Algorithm and Validation Study.Journal of medical Internet research · 2026Article
- The Evaluation of Transformer Models for the Detection of Adverse Drug Events: A Benchmark Study Using Dutch Free-Text Documents of Hospitalized Patients.Drug safety · 2026Article
- Artificial Intelligence for Drug Safety Across the Lifecycle and Decision Type: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Portability of a text mining algorithm for detecting adverse drug reactions in electronic health records across diverse patient groups in two Dutch hospitals.PLOS digital health · 2026Article
- Developing a SNOMED CT-Based Value Set to Document Symptoms and Diagnoses for Adverse Drug Events: Mixed Methods Study.JMIR medical informatics · 2025Article
- Artificial intelligence in pharmacovigilance: advancing drug safety monitoring and regulatory integration.Therapeutic advances in drug safety · 2025Review
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Authors and funding
8 authors.
Funding
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Abstract
Objective: Adverse drug reactions (ADRs) are a significant healthcare concern. They are often documented as free text in electronic health records (EHRs), making them challenging to use in clinical decision support systems (CDSS). The study aimed to develop a text mining algorithm to identify ADRs in free text of Dutch EHRs. Materials and Methods: In Phase I, our previously developed CDSS algorithm was recoded and improved upon with the same relatively large dataset of 35 000 notes (Step A), using R to identify possible ADRs with Medical Dictionary for Regulatory Activities (MedDRA) terms and the related Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) (Step B). In Phase II, 6 existing text-mining R-scripts were used to detect and present unique ADRs, and positive predictive value (PPV) and sensitivity were observed. Results: In Phase IA, the recoded algorithm performed better than the previously developed CDSS algorithm, resulting in a PPV of 13% and a sensitivity of 93%. For The sensitivity for serious ADRs was 95%. The algorithm identified 58 additional possible ADRs. In Phase IB, the algorithm achieved a PPV of 10%, a sensitivity of 86%, and an F-measure of 0.18. In Phase II, four R-scripts enhanced the sensitivity and PPV of the algorithm, resulting in a PPV of 70%, a sensitivity of 73%, an F-measure of 0.71, and a 63% sensitivity for serious ADRs. Discussion and Conclusion: The recoded Dutch algorithm effectively identifies ADRs from free-text Dutch EHRs using R-scripts and MedDRA/SNOMED-CT. The study details its limitations, highlighting the algorithm's potential and significant improvements.
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