Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique.
Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult. In these circumstances, natural language processing (NLP) techniques can facilitate automatic information extraction...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 1; pp. 80 - 91 |
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| Autores principales: | , , , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2023
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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=162233248&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162233248 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2023 vid: 36 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162233248 162233248 162233248 10.1007/s10278-022-00692-x 162233248 ppf: 80 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique. aug: au: Moezzi, Seyed Ali Reza Ghaedi, Abdolrahman Rahmanian, Mojdeh Mousavi, Seyedeh Zahra Sami, Ashkan affil: Department of Computer Science and Engineering and IT, Shiraz University, Shiraz, Iran sug: subj: Deep Learning Natural Language Processing Radiology Information Systems Reports Documentation Neural Networks (Computer) Medical Literature Nomenclature ab: Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult. In these circumstances, natural language processing (NLP) techniques can facilitate automatic information extraction and transformation of free-text formats to structured data. In recent years, deep learning (DL)-based models have been adapted for NLP experiments with promising results. Despite the significant potential of DL models based on artificial neural networks (ANN) and convolutional neural networks (CNN), the models face some limitations to implement in clinical practice. Transformers, another new DL architecture, have been increasingly applied to improve the process. Therefore, in this study, we propose a transformer-based fine-grained named entity recognition (NER) architecture for clinical information extraction. We collected 88 abdominopelvic sonography reports in free-text formats and annotated them based on our developed information schema. The text-to-text transfer transformer model (T5) and Scifive, a pre-trained domain-specific adaptation of the T5 model, were applied for fine-tuning to extract entities and relations and transform the input into a structured format. Our transformer-based model in this study outperformed previously applied approaches such as ANN and CNN models based on ROUGE-1, ROUGE-2, ROUGE-L, and BLEU scores of 0.816, 0.668, 0.528, and 0.743, respectively, while providing an interpretable structured report. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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