ArticleJournal of biomedical informatics2023
Contextualized medication information extraction using Transformer-based deep learning architectures.
Article in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
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- A study of large language models for patient information extraction: Model architecture, fine-tuning strategy, and multi-task instruction tuning.Journal of biomedical informatics · 2026Article
- AI-Based Automation for Medication Reconciliation: Scoping Review.Journal of medical Internet research · 2026Article
- Evaluation of ChatGPT-4o and DeepSeek as tools for orthodontic health literacy in public dental education.Scientific reports · 2025Article
- Assessment and Integration of Large Language Models for Automated Electronic Health Record Documentation in Emergency Medical Services.Journal of medical systems · 2025Article
- Extracting Multifaceted Characteristics of Patients With Chronic Disease Comorbidity: Framework Development Using Large Language Models.JMIR medical informatics · 2025Article
- Twenty-Five Years of Evolution and Hurdles in Electronic Health Records and Interoperability in Medical Research: Comprehensive Review.Journal of medical Internet research · 2025Review
- How LLMs can advance safety case intake-points to consider and insights from a proof of concept.Therapeutic advances in drug safety · 2025Review
- Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis.Journal of healthcare informatics research · 2024Article
- A Joint Classification Method for COVID-19 Lesions Based on Deep Learning and Radiomics.Tomography (Ann Arbor, Mich.) · 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
- Zero-shot learning to extract assessment criteria and medical services from the preventive healthcare guidelines using large language models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Clinical natural language processing for secondary uses.Journal of biomedical informatics · 2024Article
- Large language models: a primer and gastroenterology applications.Therapeutic advances in gastroenterology · 2024Review
- Opportunities and Risks of Large Language Models in Psychiatry.NPP - digital psychiatry and neuroscience · 2024Article
- What can artificial intelligence do for EUS?Endoscopic ultrasoundArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveTo develop a natural language processing (NLP) system to extract medications and contextual information that help understand drug changes. This project is part of the 2022 n2c2 challenge. MATERIALS AND
methodsWe developed NLP systems for medication mention extraction, event classification (indicating medication changes discussed or not), and context classification to classify medication changes context into 5 orthogonal dimensions related to drug changes. We explored 6 state-of-the-art pretrained transformer models for the three subtasks, including GatorTron, a large language model pretrained using > 90 billion words of text (including > 80 billion words from > 290 million clinical notes identified at the University of Florida Health). We evaluated our NLP systems using annotated data and evaluation scripts provided by the 2022 n2c2 organizers.
resultsOur GatorTron models achieved the best F1-scores of 0.9828 for medication extraction (ranked 3rd), 0.9379 for event classification (ranked 2nd), and the best micro-average accuracy of 0.9126 for context classification. GatorTron outperformed existing transformer models pretrained using smaller general English text and clinical text corpora, indicating the advantage of large language models.
conclusionThis study demonstrated the advantage of using large transformer models for contextual medication information extraction 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.