ArticleJournal of the American Medical Informatics Association : JAMIA2020
An ensemble of neural models for nested adverse drug events and medication extraction with subwords.
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 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Artificial Intelligence Methods to Pharmacy Data for Cancer Surveillance and Epidemiology Research: A Systematic Review.JCO clinical cancer informatics · 2020Pooled it
- AI-Based Automation for Medication Reconciliation: Scoping Review.Journal of medical Internet research · 2026Article
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- Clinical concept and relation extraction using prompt-based machine reading comprehension.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Extracting medication changes in clinical narratives using pre-trained language models.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
- Adverse drug event detection using natural language processing: A scoping review of supervised learning methods.PloS one · 2023Article
- A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.NPJ digital medicine · 2022Review
- MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra.Biomolecules · 2021Article
- NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding.NPJ systems biology and applications · 2021Article
- Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method.JMIRx med · 2021Article
- Extracting Drug Names and Associated Attributes From Discharge Summaries: Text Mining Study.JMIR medical informatics · 2021Article
- 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
- Advancing the state of the art in automatic extraction of adverse drug events from narratives.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- sig2db: a Workflow for Processing Natural Language from Prescription Instructions for Clinical Data Warehouses.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods.Journal of the American Medical Informatics Association : JAMIA · 2020Article
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4 authors.
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Abstract
objectiveThis article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives, which was developed for the 2018 n2c2 Shared Task Track 2. MATERIALS AND
methodsWe designed a neural model to tackle both nested (entities embedded in other entities) and polysemous entities (entities annotated with multiple semantic types) based on MIMIC III discharge summaries. To better represent rare and unknown words in entities, we further tokenized the MIMIC III data set by splitting the words into finer-grained subwords. We finally combined all the models to boost the performance. Additionally, we implemented a featured-based conditional random field model and created an ensemble to combine its predictions with those of the neural model.
resultsOur method achieved 92.78% lenient micro F1-score, with 95.99% lenient precision, and 89.79% lenient recall, respectively. Experimental results showed that combining the predictions of either multiple models, or of a single model with different settings can improve performance. DISCUSSION: Analysis of the development set showed that our neural models can detect more informative text regions than feature-based conditional random field models. Furthermore, most entity types significantly benefit from subword representation, which also allows us to extract sparse entities, especially nested entities.
conclusionThe overall results have demonstrated that the ensemble method can accurately recognize entities, including nested and polysemous entities. Additionally, our method can recognize sparse entities by reconsidering the clinical narratives at a finer-grained subword level, rather than at the word level.
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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.