ArticleJournal of the American Medical Informatics Association : JAMIA2021
UMLS-based data augmentation for natural language processing of clinical research literature.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Screening anxiety via contrastive autobiographical recall.Frontiers in digital health · 2026Article
- Subset selection based fusion for biomedical information retrieval tasks.BMC bioinformatics · 2025Article
- Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review.Findings of ACL. ACL · 2025Article
- SPIRIT-CONSORT-TM: a corpus for assessing transparency of clinical trial protocol and results publications.Scientific data · 2025Article
- SPIRIT-CONSORT-TM: a corpus for assessing transparency of clinical trial protocol and results publications.medRxiv : the preprint server for health sciences · 2025Article
- Google trend analysis of the Indian population reveals a panel of seasonally sensitive comorbid symptoms with implications for monitoring the seasonally sensitive human population.Population health metrics · 2024Article
- Text classification models for assessing the completeness of randomized controlled trial publications based on CONSORT reporting guidelines.Scientific reports · 2024Article
- Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models.Findings of ACL. ACL · 2024Article
- Enhancing the coverage of SemRep using a relation classification approach.Journal of biomedical informatics · 2024Article
- Artificial Intelligence and IBD: Where are We Now and Where Will We Be in the Future?Current gastroenterology reports · 2024Review
- CONSORT-TM: Text classification models for assessing the completeness of randomized controlled trial publications.medRxiv : the preprint server for health sciences · 2024Article
- Automatic categorization of self-acknowledged limitations in randomized controlled trial publications.Journal of biomedical informatics · 2024Article
- Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence.Frontiers in bioengineering and biotechnology · 2024Article
- Article
- Towards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning approach.Bioinformatics (Oxford, England) · 2023Article
- Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- MedLexSp - a medical lexicon for Spanish medical natural language processing.Journal of biomedical semantics · 2023Article
- Artificial Intelligence for Disease Assessment in Inflammatory Bowel Disease: How Will it Change Our Practice?Gastroenterology · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveThe study sought to develop and evaluate a knowledge-based data augmentation method to improve the performance of deep learning models for biomedical natural language processing by overcoming training data scarcity. MATERIALS AND
methodsWe extended the easy data augmentation (EDA) method for biomedical named entity recognition (NER) by incorporating the Unified Medical Language System (UMLS) knowledge and called this method UMLS-EDA. We designed experiments to systematically evaluate the effect of UMLS-EDA on popular deep learning architectures for both NER and classification. We also compared UMLS-EDA to BERT.
resultsUMLS-EDA enables substantial improvement for NER tasks from the original long short-term memory conditional random fields (LSTM-CRF) model (micro-F1 score: +5%, + 17%, and +15%), helps the LSTM-CRF model (micro-F1 score: 0.66) outperform LSTM-CRF with transfer learning by BERT (0.63), and improves the performance of the state-of-the-art sentence classification model. The largest gain on micro-F1 score is 9%, from 0.75 to 0.84, better than classifiers with BERT pretraining (0.82).
conclusionsThis study presents a UMLS-based data augmentation method, UMLS-EDA. It is effective at improving deep learning models for both NER and sentence classification, and contributes original insights for designing new, superior deep learning approaches for low-resource biomedical domains.
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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.