ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2017
Discovering adverse drug events combining spontaneous reports with electronic medical records: a case study of conventional DMARDs and biologics for rheumatoid arthritis.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed.
- Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.Drug safety · 2025Article
- Advancing rheumatology with natural language processing: insights and prospects from a systematic review.Rheumatology advances in practice · 2024Review
- Integrating electronic health records with other data sources for postmarket drug safety signal identification: a review.Frontiers in drug safety and regulation · 2024Review
- Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review.Drug safety · 2023Article
- Target Adverse Event Profiles for Predictive Safety in the Postmarket Setting.Clinical pharmacology and therapeutics · 2021Article
- Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients.Journal of healthcare informatics research · 2019Article
- Data Science for Child Health.The Journal of pediatrics · 2019Review
- Detecting Pharmacovigilance Signals Combining Electronic Medical Records With Spontaneous Reports: A Case Study of Conventional Disease-Modifying Antirheumatic Drugs for Rheumatoid Arthritis.Frontiers in pharmacology · 2018Article
- Challenges of managing pediatric polypharmacy in a pediatric complex care program: A qualitative pilot study.Journal of the American Pharmacists Association : JAPhAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of multiple data sources has been preferred in the surveillance of adverse drug events due to shortcomings of using only a single source. In this study, we proposed a framework where the ADEs associated with interested drugs are systematically discovered from the FDA's Adverse Event Reporting System (AERS), and then validated through mining unstructured clinical notes from Electronic Medical Records (EMRs). This framework has two features. First, a higher priority was given to clinical practice during signal detection and validation. Second, the normalization by NLP facilitated the interoperation between AERS-DM and the EMR. To demonstrate this methodology, we investigated potential ADEs associated with drugs (class level) for rheumatoid arthritis (RA) patients. The results demonstrated the feasibility and sufficient accuracy of the framework. The framework can serve as the interface between the informatics domain and the medical domain to facilitate ADE discovery.
Identifiers
28815115PMC5543355What OpenQuestion holds
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.