ArticleBritish journal of clinical pharmacology2022
Identifying adverse drug reactions from free-text electronic hospital health record notes.
Article in British journal of clinical pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- 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
- Analytical approaches for medication management for patient safety: a scoping review.npj health systems · 2025Article
- Effectiveness of Transformer-Based Large Language Models in Identifying Adverse Drug Reaction Relations from Unstructured Discharge Summaries in Singapore.Drug safety · 2025Article
- Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.Drug safety · 2025Article
- PretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports.Journal of cheminformatics · 2025Article
- Development of a text mining algorithm for identifying adverse drug reactions in electronic health records.JAMIA open · 2024Article
- Validation and clinical discovery demonstration of breast cancer data from a real-world data extraction platform.JAMIA open · 2024Article
- Article
- Disponibilité de l'information médicale requise pour la déclaration d'une réaction indésirable médicamenteuse à Santé Canada: une étude exploratoire.The Canadian journal of hospital pharmacy · 2024Article
- Pharmacoepidemiology: An Overview.Journal of clinical medicine · 2023Review
- Combining text mining with clinical decision support in clinical practice: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Quality Improvement in the Preoperative Evaluation: Accuracy of an Automated Clinical Decision Support System to Calculate CHAMedicina (Kaunas, Lithuania) · 2022Article
- Integration of Risk Scores and Integration Capability in Electronic Patient Records.Applied clinical informatics · 2022Article
- Identifying adverse drug reactions from free-text electronic hospital health record notes.British journal of clinical pharmacology · 2022Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdverse drug reactions (ADRs) are estimated to be the fifth cause of hospital death. Up to 50% are potentially preventable and a significant number are recurrent (reADRs). Clinical decision support systems have been used to prevent reADRs using structured reporting concerning the patient's ADR experience, which in current clinical practice is poorly performed. Identifying ADRs directly from free text in electronic health records (EHRs) could circumvent this.
aimTo develop strategies to identify ADRs from free-text notes in electronic hospital health records.
methodsIn stage I, the EHRs of 10 patients were reviewed to establish strategies for identifying ADRs. In stage II, complete EHR histories of 45 patients were reviewed for ADRs and compared to the strategies programmed into a rule-based model. ADRs were classified using MedDRA and included in the study if the Naranjo causality score was ≥1. Seriousness was assessed using the European Medicine Agency's important medical event list.
resultsIn stage I, two main search strategies were identified: keywords indicating an ADR and specific prepositions followed by medication names. In stage II, the EHRs contained a median of 7.4 (range 0.01-18) years of medical history covering over 35 000 notes. A total of 318 unique ADRs were identified of which 63 were potentially serious and 179 (sensitivity 57%) were identified by the rule. The method falsely identified 377 ADRs (positive predictive value 32%). However, it also identified an additional eight ADRs.
conclusionTwo key strategies were developed to identify ADRs from hospital EHRs using free-text notes. The results appear promising and warrant further study.
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