ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2019
Relation Extraction from Clinical Narratives Using Pre-trained Language Models.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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The trial behind it
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Who cites it
29 citing papers in PubMed, 56 citations in OpenAlex.
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- Predicting ICU in-hospital mortality from text-encoded structured EHR data using adaptive transformer layer fusion.iScience · 2026Article
- AI assistance in tumor multidisciplinary teams.ESMO real world data and digital oncology · 2026Review
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- LitTx: A New Treatment Relation Extraction Dataset.LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation · 2026Article
- Efficient extraction of medication information from clinical notes: an evaluation in 2 languages.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Domain and Language adaptive pre-training of BERT models for Korean-English bilingual clinical text analysis.BMC medical informatics and decision making · 2025Article
- Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice.PLOS digital health · 2025Article
- Enhancing Cross-Domain Generalizability in Social Determinants of Health Extraction with Prompt-Tuning Large Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Classification of periodontitis stage and grade using natural language processing techniques.PLOS digital health · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Automated Extraction of Patient-Centered Outcomes After Breast Cancer Treatment: An Open-Source Large Language Model-Based Toolkit.JCO clinical cancer informatics · 2024Article
- Leveraging shortest dependency paths in low-resource biomedical relation extraction.BMC medical informatics and decision making · 2024Article
- Explainable text-tabular models for predicting mortality risk in companion animals.Scientific reports · 2024Article
- Thyroidkeeper: a healthcare management system for patients with thyroid diseases.Health information science and systems · 2023Article
- Predicting relations between SOAP note sections: The value of incorporating a clinical information model.Journal of biomedical informatics · 2023Article
- Clinical Artificial Intelligence: Design Principles and Fallacies.Clinics in laboratory medicine · 2023Review
- Precision information extraction for rare disease epidemiology at scale.Journal of translational medicine · 2023Article
- A large language model for electronic health records.NPJ digital medicine · 2022Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
Natural language processing (NLP) is useful for extracting information from clinical narratives, and both traditional machine learning methods and more-recent deep learning methods have been successful in various clinical NLP tasks. These methods often depend on traditional word embeddings that are outputs of language models (LMs). Recently, methods that are directly based on pre-trained language models themselves, followed by fine-tuning on the LMs (e.g. the Bidirectional Encoder Representations from Transformers (BERT)), have achieved state-of-the-art performance on many NLP tasks. Despite their success in the open domain and biomedical literature, these pre-trained LMs have not yet been applied to the clinical relation extraction (RE) task. In this study, we developed two different implementations of the BERT model for clinical RE tasks. Our results show that our tuned LMs outperformed previous state-of-the-art RE systems in two shared tasks, which demonstrates the potential of LM-based methods on the RE task.
Indexed as
Identifiers
32308921PMC7153059W3027260829What 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.