UMLS-based data augmentation for natural language processing of clinical research literature.
Objective: The 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 Methods: We extended the easy data augmentation (EDA) method...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 28; no. 4; pp. 812 - 824 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Apr2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149414964&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149414964 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Apr2021 vid: 28 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 149414964 149414964 NLM33367705 10.1093/jamia/ocaa309 NLM33367705 149414964 ppf: 812 ppct: 12 formats: tig: atl: UMLS-based data augmentation for natural language processing of clinical research literature. aug: au: Kang, Tian Perotte, Adler Tang, Youlan Ta, Casey Weng, Chunhua affil: Department of Biomedical Informatics, Columbia University , New York, New York, USA sug: subj: Research, Medical Information Retrieval Methods Natural Language Processing Unified Medical Language System Short Portable Mental Status Questionnaire Clinical Assessment Tools Scales ab: Objective: The 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 Methods: We 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.Results: UMLS-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).Conclusions: This 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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