Three-Branch BERT-Based Text Classification Network for Gastroscopy Diagnosis Text
During a hospital visit, a significant volume of Gastroscopy Diagnostic Text (GDT) data are produced, representing the unstructured gastric medical records of patients undergoing gastroscopy. As such, GDTs play a crucial role in evaluating the patient’s health, shaping treatment plans, and schedulin...
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| Veröffentlicht in: | International journal of crowd science Jg. 8; H. 1; S. 56 - 63 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Tsinghua University Press
01.03.2024
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| ISSN: | 2398-7294, 2398-7294 |
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| Abstract | During a hospital visit, a significant volume of Gastroscopy Diagnostic Text (GDT) data are produced, representing the unstructured gastric medical records of patients undergoing gastroscopy. As such, GDTs play a crucial role in evaluating the patient’s health, shaping treatment plans, and scheduling follow-up visits. However, given the free-text nature of GDTs, which lack a formal structure, physicians often find it challenging to extract meaningful insights from them. Furthermore, while deep learning has made significant strides in the medical domain, to our knowledge, there are not any readily available text-based pre-trained models tailored for GDT classification and analysis. To address this gap, we introduce a Bidirectional Encoder Representations from Transformers (BERT) based three-branch classification network tailored for GDTs. We leverage the robust representation capabilities of the BERT pre-trained model to deeply encode the texts. A unique three-branch decoder structure is employed to pinpoint lesion sites and determine cancer stages. Experimental outcomes validate the efficacy of our approach in GDT classification, with a precision of 0.993 and a recall of 0.784 in the early cancer category. In pinpointing cancer lesion sites, the weighted F1 score achieved was 0.849. |
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| AbstractList | During a hospital visit, a significant volume of Gastroscopy Diagnostic Text (GDT) data are produced, representing the unstructured gastric medical records of patients undergoing gastroscopy. As such, GDTs play a crucial role in evaluating the patient’s health, shaping treatment plans, and scheduling follow-up visits. However, given the free-text nature of GDTs, which lack a formal structure, physicians often find it challenging to extract meaningful insights from them. Furthermore, while deep learning has made significant strides in the medical domain, to our knowledge, there are not any readily available text-based pre-trained models tailored for GDT classification and analysis. To address this gap, we introduce a Bidirectional Encoder Representations from Transformers (BERT) based three-branch classification network tailored for GDTs. We leverage the robust representation capabilities of the BERT pre-trained model to deeply encode the texts. A unique three-branch decoder structure is employed to pinpoint lesion sites and determine cancer stages. Experimental outcomes validate the efficacy of our approach in GDT classification, with a precision of 0.993 and a recall of 0.784 in the early cancer category. In pinpointing cancer lesion sites, the weighted F1 score achieved was 0.849. |
| Author | Wang, Zhichao Zheng, Xiangwei Zhang, Mingzhe Zhang, Jinsong |
| Author_xml | – sequence: 1 givenname: Zhichao surname: Wang fullname: Wang, Zhichao organization: School of Information Science and Engineering, Shandong Normal University,Jinan,China,250358 – sequence: 2 givenname: Xiangwei surname: Zheng fullname: Zheng, Xiangwei organization: School of Information Science and Engineering, Shandong Normal University,Jinan,China,250358 – sequence: 3 givenname: Jinsong surname: Zhang fullname: Zhang, Jinsong organization: School of Information Science and Engineering, Shandong Normal University,Jinan,China,250358 – sequence: 4 givenname: Mingzhe surname: Zhang fullname: Zhang, Mingzhe organization: School of Information Science and Engineering, Shandong Normal University,Jinan,China,250358 |
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| CitedBy_id | crossref_primary_10_1016_j_bspc_2025_107891 crossref_primary_10_1016_j_dss_2025_114421 crossref_primary_10_1016_j_eswa_2025_126583 crossref_primary_10_1016_j_im_2025_104219 crossref_primary_10_3390_app14188388 crossref_primary_10_1007_s11042_024_20557_5 |
| Cites_doi | 10.48550/ARXIV.1907.11692 10.3389/fpubh.2022.925011 10.1023/A:1010933404324 10.1016/j.eswa.2016.03.045 10.3390/biomedicines9101448 10.1145/312624.312647 10.1109/ACCESS.2021.3049734 10.1016/j.jksuci.2022.02.025 10.1007/BF00116251 10.48550/ARXIV.1706.03762 10.1007/978-3-642-24797-2_4 10.1145/3439726 10.1007/978-1-4842-2766-4_7 10.1016/j.jksuci.2023.101610 10.1109/TSMC.2021.3096974 10.1016/j.engappai.2016.02.002 |
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| Title | Three-Branch BERT-Based Text Classification Network for Gastroscopy Diagnosis Text |
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