ArticleJournal of the American Medical Informatics Association : JAMIA2020
Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- AI-Based Automation for Medication Reconciliation: Scoping Review.Journal of medical Internet research · 2026Article
- Machine learning-based prediction of ventilator therapeutic pressure for optimized CPAP titration.BMC medical informatics and decision making · 2026Article
- Large Language Models for Clinical Narrative Processing: Methods, Applications, and Challenges.Methods and protocols · 2026Article
- Leveraging Large Language Models in Extracting Drug Safety Information from Prescription Drug Labels.Drug safety · 2026Article
- Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study.JMIR formative research · 2025Article
- Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.The journal of prevention of Alzheimer's disease · 2025Article
- Extracting Pulmonary Nodules and Nodule Characteristics from Radiology Reports of Lung Cancer Screening Patients Using Transformer Models.Journal of healthcare informatics research · 2024Article
- Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction.Journal of medical systems · 2024Article
- Sample Size Considerations for Fine-Tuning Large Language Models for Named Entity Recognition Tasks: Methodological Study.JMIR AI · 2024Article
- Model tuning or prompt Tuning? a study of large language models for clinical concept and relation extraction.Journal of biomedical informatics · 2024Article
- Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.Journal of biomedical informatics · 2024Article
- Named Entity Recognition in Electronic Health Records: A Methodological Review.Healthcare informatics research · 2023Article
- Clinical concept and relation extraction using prompt-based machine reading comprehension.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Contextualized medication information extraction using Transformer-based deep learning architectures.Journal of biomedical informatics · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Semantically oriented EHR navigation with a patient specific knowledge base and a clinical context ontology.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Adverse drug event detection using natural language processing: A scoping review of supervised learning methods.PloS one · 2023Article
- A large language model for electronic health records.NPJ digital medicine · 2022Article
- Entity relation extraction in the medical domain: based on data augmentation.Annals of translational medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveTo develop a natural language processing system that identifies relations of medications with adverse drug events from clinical narratives. This project is part of the 2018 n2c2 challenge. MATERIALS AND
methodsWe developed a novel clinical named entity recognition method based on an recurrent convolutional neural network and compared it to a recurrent neural network implemented using the long-short term memory architecture, explored methods to integrate medical knowledge as embedding layers in neural networks, and investigated 3 machine learning models, including support vector machines, random forests and gradient boosting for relation classification. The performance of our system was evaluated using annotated data and scripts provided by the 2018 n2c2 organizers.
resultsOur system was among the top ranked. Our best model submitted during this challenge (based on recurrent neural networks and support vector machines) achieved lenient F1 scores of 0.9287 for concept extraction (ranked third), 0.9459 for relation classification (ranked fourth), and 0.8778 for the end-to-end relation extraction (ranked second). We developed a novel named entity recognition model based on a recurrent convolutional neural network and further investigated gradient boosting for relation classification. The new methods improved the lenient F1 scores of the 3 subtasks to 0.9292, 0.9633, and 0.8880, respectively, which are comparable to the best performance reported in this challenge.
conclusionThis study demonstrated the feasibility of using machine learning methods to extract the relations of medications with adverse drug events from clinical narratives.
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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.