ArticleJournal of the American Medical Informatics Association : JAMIA
A method for controlling complex confounding effects in the detection of adverse drug reactions using electronic health records.
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 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning methods for predicting adverse drug events: A systematic review.British journal of clinical pharmacology · 2026Pooled it
- Systematic review of AI-based models in pharmacoepidemiology for adverse drug event prediction and detection.Frontiers in drug safety and regulation · 2026Pooled it
- Machine Learning for Predicting Postoperative Functional Disability and Mortality Among Older Patients With Cancer: Retrospective Cohort Study.JMIR aging · 2025Article
- Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review.Drug safety · 2023Article
- Association of 5α-Reductase Inhibitor Prescription With Bladder Cancer Progression in Males in South Korea.JAMA network open · 2023Article
- A simple neural vector space model for medical concept normalization using concept embeddings.Journal of biomedical informatics · 2022Article
- Utility of a Laboratory Alert System for Detecting Adverse Drug Reactions in Hospitalised Patients: Hyponatremia and Rhabdomyolysis.Frontiers in pharmacology · 2022Article
- Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts: Development and Usability Study.Journal of medical Internet research · 2020Article
- Mapping anatomical related entities to human body parts based on wikipedia in discharge summaries.BMC bioinformatics · 2019Article
- Development of a Controlled Vocabulary-Based Adverse Drug Reaction Signal Dictionary for Multicenter Electronic Health Record-Based Pharmacovigilance.Drug safety · 2019Article
- Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model.Journal of medical Internet research · 2019Article
- Developing a Data-driven Medication Indication Knowledge Base using a Large Scale Medical Claims Database.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- A Hybrid Normalization Method for Medical Concepts in Clinical Narrative using Semantic Matching.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media.Briefings in bioinformatics · 2018Review
- An MCEM Framework for Drug Safety Signal Detection and Combination from Heterogeneous Real World Evidence.Scientific reports · 2018Article
- Article
- Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record.Applied clinical informatics · 2017Article
- Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text.Yearbook of medical informatics · 2017Review
- -Omic and Electronic Health Record Big Data Analytics for Precision Medicine.IEEE transactions on bio-medical engineering · 2017Review
- Literature-Based Discovery of Confounding in Observational Clinical Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveElectronic health records (EHRs) contain information to detect adverse drug reactions (ADRs), as they contain comprehensive clinical information. A major challenge of using comprehensive information involves confounding. We propose a novel data-driven method to identify ADR signals accurately by adjusting for confounders. MATERIALS AND
methodsWe focused on two serious ADRs, rhabdomyolysis and pancreatitis, and used information in 264,155 unique patient records. We identified an ADR using established criteria, selected potential confounders, and then used penalized logistic regressions to estimate confounder-adjusted ADR associations. A reference standard was created to evaluate and compare the precision of the proposed method and four others.
resultsPrecision was 83.3% for rhabdomyolysis and 60.8% for pancreatitis when using the proposed method, and we identified several drug safety signals that are interesting for further clinical review. DISCUSSION: The proposed method effectively estimated ADR associations after adjusting for confounders. A main cause of error was probably due to the nature of the dataset in that a substantial number of patients had a single visit only and, therefore, it was not possible to determine correctly the appropriate sequence of events for them. It is likely that performance will be improved with use of EHR data that contain more longitudinal records.
conclusionsThis data-driven method is effective in controlling for confounding, resulting in either a higher or similar precision when compared with four comparators, has the unique ability to provide insight into confounders for each specific medication-ADR pair, and can be easily adapted to other EHR systems.
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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.