ArticleClinical pharmacology and therapeutics2012
Detection of pharmacovigilance-related adverse events using electronic health records and automated methods.
Article in Clinical pharmacology and therapeutics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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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.
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Who cites it
62 citing papers in PubMed.
- Assessing pediatric clinician adherence to the guidelines for prevention of peanut allergy: a natural language processing study.BMC medical informatics and decision making · 2025Trial
- Development and Application of an Algorithm for Statin-Induced Myopathy Based on Electronic Health Record-Derived Structured Elements.Pharmacoepidemiology and drug safety · 2025Article
- Using electronic health records for clinical pharmacology research: Challenges and considerations.Clinical and translational science · 2024Review
- Initial development of tools to identify child abuse and neglect in pediatric primary care.BMC medical informatics and decision making · 2023Article
- How can artificial intelligence optimize value-based contracting?Journal of pharmaceutical policy and practice · 2022Article
- Tasks as needs: reframing the paradigm of clinical natural language processing research for real-world decision support.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
- An atomic approach to the design and implementation of a research data warehouse.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Utility of a Laboratory Alert System for Detecting Adverse Drug Reactions in Hospitalised Patients: Hyponatremia and Rhabdomyolysis.Frontiers in pharmacology · 2022Article
- Real-World Metastatic Renal Cell Carcinoma Treatment Patterns and Clinical Outcomes in The Netherlands.Frontiers in pharmacology · 2022Article
- Comparing automated vs. manual data collection for COVID-specific medications from electronic health records.International journal of medical informatics · 2022Article
- Artificial Intelligence for Identifying the Prevention of Medication Incidents Causing Serious or Moderate Harm: An Analysis Using Incident Reporters' Views.International journal of environmental research and public health · 2021Article
- Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Towards a symbiotic relationship between big data, artificial intelligence, and hospital pharmacy.Journal of pharmaceutical policy and practice · 2020Article
- Adverse drug event detection using reason assignments in FDA drug labels.Journal of biomedical informatics · 2020Article
- An Electronic Health Record Text Mining Tool to Collect Real-World Drug Treatment Outcomes: A Validation Study in Patients With Metastatic Renal Cell Carcinoma.Clinical pharmacology and therapeutics · 2020Observational
- Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models.JMIR medical informatics · 2020Article
- Automation Opportunities in Pharmacovigilance: An Industry Survey.Pharmaceutical medicine · 2020Article
- Identifying Herbal Adverse Events From Spontaneous Reporting Systems Using Taxonomic Name Resolution Approach.Bioinformatics and biology insights · 2020Article
- A study of deep learning approaches for medication and adverse drug event extraction from clinical text.Journal of the American Medical Informatics Association : JAMIA · 2020Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Electronic health records (EHRs) are an important source of data for detection of adverse drug reactions (ADRs). However, adverse events are frequently due not to medications but to the patients' underlying conditions. Mining to detect ADRs from EHR data must account for confounders. We developed an automated method using natural-language processing (NLP) and a knowledge source to differentiate cases in which the patient's disease is responsible for the event rather than a drug. Our method was applied to 199,920 hospitalization records, concentrating on two serious ADRs: rhabdomyolysis (n = 687) and agranulocytosis (n = 772). Our method automatically identified 75% of the cases, those with disease etiology. The sensitivity and specificity were 93.8% (confidence interval: 88.9-96.7%) and 91.8% (confidence interval: 84.0-96.2%), respectively. The method resulted in considerable saving of time: for every 1 h spent in development, there was a saving of at least 20 h in manual review. The review of the remaining 25% of the cases therefore became more feasible, allowing us to identify the medications that had caused the ADRs.
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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.