ArticleJournal of the American Medical Informatics Association : JAMIA2020
Clinical concept extraction using transformers.
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 61 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
61 citing papers in PubMed, 1 synthesis or guideline pooled it, 149 citations in OpenAlex.
- Press Releases of Drug-Related Randomized Trial Results Prior to Publication in High-Impact Journals: an Observational Study.Journal of general internal medicine · 2023Pooled it
- Multilingual Evidence-Based Question-Answering for Stroke Discharge Summaries: Study of Cross-Lingual Heterogeneity in Clinical Reports.Journal of medical Internet research · 2026Article
- 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
- Clinical parameters associated with blood pressure in patients with primary open-angle glaucoma and exfoliation glaucoma.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Multimodal text-audio sentiment in clinical aphasia speech using NLP.Frontiers in digital health · 2026Article
- Extracting Social Determinants of Health from Dental Clinical Notes.Applied clinical informatics · 2025Article
- Article
- Mapping the Advanced-Stage Epithelial Ovarian Cancer Landscape Goes Beyond Words: Two Large Language Models, Eight Tasks, One Journey.Journal of clinical medicine · 2025Article
- Leveraging transformer models to predict cognitive impairment: accuracy, efficiency, and interpretability.BMC public 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
- Toward a Computable Phenotype for Determining Eligibility of Lung Cancer Screening Using Electronic Health Records.JCO clinical cancer informatics · 2025Article
- Identifying Opioid Overdose and Opioid Use Disorder and Related Information from Clinical Narratives Using Large Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Multimodal BEHRT: transformers for multimodal electronic health records to predict breast cancer prognosis.Frontiers in oncology · 2025Article
- Multifaceted Natural Language Processing Task-Based Evaluation of Bidirectional Encoder Representations From Transformers Models for Bilingual (Korean and English) Clinical Notes: Algorithm Development and Validation.JMIR medical informatics · 2024Article
- Article
- Privacy-preserving large language models for structured medical information retrieval.NPJ digital medicine · 2024Article
- 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 GPT-based EHR modeling system for unsupervised novel disease detection.Journal of biomedical informatics · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveThe goal of this study is to explore transformer-based models (eg, Bidirectional Encoder Representations from Transformers [BERT]) for clinical concept extraction and develop an open-source package with pretrained clinical models to facilitate concept extraction and other downstream natural language processing (NLP) tasks in the medical domain.
methodsWe systematically explored 4 widely used transformer-based architectures, including BERT, RoBERTa, ALBERT, and ELECTRA, for extracting various types of clinical concepts using 3 public datasets from the 2010 and 2012 i2b2 challenges and the 2018 n2c2 challenge. We examined general transformer models pretrained using general English corpora as well as clinical transformer models pretrained using a clinical corpus and compared them with a long short-term memory conditional random fields (LSTM-CRFs) mode as a baseline. Furthermore, we integrated the 4 clinical transformer-based models into an open-source package. RESULTS AND
conclusionThe RoBERTa-MIMIC model achieved state-of-the-art performance on 3 public clinical concept extraction datasets with F1-scores of 0.8994, 0.8053, and 0.8907, respectively. Compared to the baseline LSTM-CRFs model, RoBERTa-MIMIC remarkably improved the F1-score by approximately 4% and 6% on the 2010 and 2012 i2b2 datasets. This study demonstrated the efficiency of transformer-based models for clinical concept extraction. Our methods and systems can be applied to other clinical tasks. The clinical transformer package with 4 pretrained clinical models is publicly available at https://github.com/uf-hobi-informatics-lab/ClinicalTransformerNER. We believe this package will improve current practice on clinical concept extraction and other tasks in the medical domain.
Indexed as
Identifiers
What 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.