Relation extraction for colorectal cancer via deep learning with entity-aware feature orthogonal decomposition
Relation extraction is significant for text structuring of colorectal cancer (CRC) pathological reports to facilitate doctors’ disease diagnoses. Although many relation extraction methods have been extensively studied for various natural language processing applications, they cannot be well transfer...
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| Vydané v: | Expert systems with applications Ročník 258; s. 125188 |
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| Hlavní autori: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
15.12.2024
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| ISSN: | 0957-4174 |
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| Abstract | Relation extraction is significant for text structuring of colorectal cancer (CRC) pathological reports to facilitate doctors’ disease diagnoses. Although many relation extraction methods have been extensively studied for various natural language processing applications, they cannot be well transferred to be applied for CRC pathological reports since CRC pathological reports have some unique characteristics. To this end, a deep learning framework is designed in this paper to extract entity relations in CRC pathological reports, which is based on an encoder–decoder architecture with entity-aware feature orthogonal decomposition. Specifically, to effectively extract semantic features of long and short entities, a two-stream encoder is designed based on an edge-aware convolutional neural network and a dimension-aware dilated convolution residual network. To alleviate the influence of the blending of subject–object features, entity-aware feature orthogonal decomposition is designed to decompose the extracted semantic features into three types, i.e. subject features, object features and subject–object shared features. A stage-wise cross entropy loss is proposed to well train the network. Comparison experiments indicated that our designed network performs well on CRC pathological texts with the performance of 92.3% F1 score, 93.1% Precision, and 91.5% Recall, outperforming the existing relation extraction models.
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•Joint relation extraction for CRC pathological reports via deep learning.•Edge-aware CNNs and dimension-aware DCRN for extracting entity features.•Entity-aware feature orthogonal decomposition for decomposing entity features.•Stage-wise cross-entropy loss is proposed to well ensure the network training.•Perform well on real CRC pathological reports with the 92.3% F1 score. |
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| AbstractList | Relation extraction is significant for text structuring of colorectal cancer (CRC) pathological reports to facilitate doctors’ disease diagnoses. Although many relation extraction methods have been extensively studied for various natural language processing applications, they cannot be well transferred to be applied for CRC pathological reports since CRC pathological reports have some unique characteristics. To this end, a deep learning framework is designed in this paper to extract entity relations in CRC pathological reports, which is based on an encoder–decoder architecture with entity-aware feature orthogonal decomposition. Specifically, to effectively extract semantic features of long and short entities, a two-stream encoder is designed based on an edge-aware convolutional neural network and a dimension-aware dilated convolution residual network. To alleviate the influence of the blending of subject–object features, entity-aware feature orthogonal decomposition is designed to decompose the extracted semantic features into three types, i.e. subject features, object features and subject–object shared features. A stage-wise cross entropy loss is proposed to well train the network. Comparison experiments indicated that our designed network performs well on CRC pathological texts with the performance of 92.3% F1 score, 93.1% Precision, and 91.5% Recall, outperforming the existing relation extraction models.
[Display omitted]
•Joint relation extraction for CRC pathological reports via deep learning.•Edge-aware CNNs and dimension-aware DCRN for extracting entity features.•Entity-aware feature orthogonal decomposition for decomposing entity features.•Stage-wise cross-entropy loss is proposed to well ensure the network training.•Perform well on real CRC pathological reports with the 92.3% F1 score. |
| ArticleNumber | 125188 |
| Author | Cai, Nian Chen, Chuanwen Feng, Jianjun Peng, Fuqiang Wang, Xiaodan Luo, Zhihao Li, Quanqing Liao, Jiacheng |
| Author_xml | – sequence: 1 givenname: Zhihao surname: Luo fullname: Luo, Zhihao email: 2112203076@mail2.gdut.edu.cn organization: School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China – sequence: 2 givenname: Jianjun orcidid: 0009-0002-8068-2138 surname: Feng fullname: Feng, Jianjun email: 3122002009@mail2.gdut.edu.cn organization: School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China – sequence: 3 givenname: Nian orcidid: 0000-0002-7826-5055 surname: Cai fullname: Cai, Nian email: cainian@gdut.edu.cn organization: School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China – sequence: 4 givenname: Xiaodan surname: Wang fullname: Wang, Xiaodan email: wangxiaodan@huayinlab.com organization: Guangzhou Huayinkang Medical Group Co., Ltd., Guangzhou 510006, China – sequence: 5 givenname: Jiacheng surname: Liao fullname: Liao, Jiacheng email: liaojiacheng@talkiin.cn organization: Foshan Talkiin Technology Co., LTD., 5th Floor, Building 1, Wanbang Commercial Plaza, Daliang Street, Shunde District, Foshan City, China – sequence: 6 givenname: Quanqing surname: Li fullname: Li, Quanqing email: liquanqing@huayinlab.com organization: Guangzhou Huayinkang Medical Group Co., Ltd., Guangzhou 510006, China – sequence: 7 givenname: Fuqiang orcidid: 0009-0003-6138-6883 surname: Peng fullname: Peng, Fuqiang email: pengfuqiang@huayinlab.com organization: Guangzhou Huayinkang Medical Group Co., Ltd., Guangzhou 510006, China – sequence: 8 givenname: Chuanwen surname: Chen fullname: Chen, Chuanwen email: chenchuanwen@huayinlab.com organization: Guangzhou Huayinkang Medical Group Co., Ltd., Guangzhou 510006, China |
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| Keywords | Relation extraction Feature orthogonal decomposition Two-stream feature extraction Pathological reports Stage-wise cross entropy Colorectal cancer |
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