ArticleJournal of the American Medical Informatics Association : JAMIA
Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 135 papers, 3 of them syntheses that pooled it.
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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
135 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Text mining applications in psychiatry: a systematic literature review.International journal of methods in psychiatric research · 2016Pooled it
- Artificial Intelligence for Opioid Safety Surveillance from Clinical Text: A Clinically Focused Review.Journal of clinical medicine · 2026Review
- Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study.JMIR formative research · 2025Article
- MDDC: An R and Python package for adverse event identification in pharmacovigilance data.Scientific reports · 2025Article
- Leveraging undecided cases in chart-reviewed phenotypes to enhance EHR-based association studies.Journal of biomedical informatics · 2025Article
- Exploring trauma recovery in nurses: a text mining and thematic analysis based on Swanson's theory of caring.BMC nursing · 2025Article
- Article
- Occurrence of Opioid-Related Neurocognitive Symptoms Associated With Long-term Opioid Therapy.The Clinical journal of pain · 2025Article
- Extraction of sleep information from clinical notes of Alzheimer's disease patients using natural language processing.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- A text mining approach to categorize patient safety event reports by medication error type.Scientific reports · 2023Article
- Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review.Drug safety · 2023Article
- Article
- Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients.BMC medical informatics and decision making · 2023Article
- Explainable detection of adverse drug reaction with imbalanced data distribution.PLoS computational biology · 2022Article
- Deep Learning for Cancer Symptoms Monitoring on the Basis of Electronic Health Record Unstructured Clinical Notes.JCO clinical cancer informatics · 2022Article
- Intelligent Telehealth in Pharmacovigilance: A Future Perspective.Drug safety · 2022Review
- Using Machine Learning for Pharmacovigilance: A Systematic Review.Pharmaceutics · 2022Review
75 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
4 authors.
Funding
Abstract
OBJECTIVE It is vital to detect the full safety profile of a drug throughout its market life. Current pharmacovigilance systems still have substantial limitations, however. The objective of our work is to demonstrate the feasibility of using natural language processing (NLP), the comprehensive Electronic Health Record (EHR), and association statistics for pharmacovigilance purposes. DESIGN Narrative discharge summaries were collected from the Clinical Information System at New York Presbyterian Hospital (NYPH). MedLEE, an NLP system, was applied to the collection to identify medication events and entities which could be potential adverse drug events (ADEs). Co-occurrence statistics with adjusted volume tests were used to detect associations between the two types of entities, to calculate the strengths of the associations, and to determine their cutoff thresholds. Seven drugs/drug classes (ibuprofen, morphine, warfarin, bupropion, paroxetine, rosiglitazone, ACE inhibitors) with known ADEs were selected to evaluate the system. RESULTS One hundred thirty-two potential ADEs were found to be associated with the 7 drugs. Overall recall and precision were 0.75 and 0.31 for known ADEs respectively. Importantly, qualitative evaluation using historic roll back design suggested that novel ADEs could be detected using our system. CONCLUSIONS This study provides a framework for the development of active, high-throughput and prospective systems which could potentially unveil drug safety profiles throughout their entire market life. Our results demonstrate that the framework is feasible although there are some challenging issues. To the best of our knowledge, this is the first study using comprehensive unstructured data from the EHR for pharmacovigilance.
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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.