Leveraging a Joint learning Model to Extract Mixture Symptom Mentions from Traditional Chinese Medicine Clinical Notes.
This paper addresses the mixture symptom mention problem which appears in the structuring of Traditional Chinese Medicine (TCM). We accomplished this by disassembling mixture symptom mentions with entity relation extraction. Over 2,200 clinical notes were annotated to construct the training set. The...
| Publicado en: | BioMed Research International pp. 1 - 8 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
3/8/2022
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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=155625245&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155625245 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/8/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 155625245 155625245 155625245 10.1155/2022/2146236 155625245 ppf: 1 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Leveraging a Joint learning Model to Extract Mixture Symptom Mentions from Traditional Chinese Medicine Clinical Notes. aug: au: Sun, Yuxin Zhao, Zhenying Wang, Zhongyi He, Haiyang Guo, Feng Luo, Yuchen Gao, Qing Wei, Ningjing Liu, Jialin Li, Guo-Zheng Liu, Ziqing affil: The Third Affiliated Hospital, Henan University of Chinese Medicine, Zhengzhou 450046, China sug: subj: Information Retrieval Models, Theoretical Signs and Symptoms Medicine, Chinese Traditional Human Data Management Documentation Software Memory Descriptive Statistics Natural Language Processing ab: This paper addresses the mixture symptom mention problem which appears in the structuring of Traditional Chinese Medicine (TCM). We accomplished this by disassembling mixture symptom mentions with entity relation extraction. Over 2,200 clinical notes were annotated to construct the training set. Then, an end-to-end joint learning model was established to extract the entity relations. A joint model leveraging a multihead mechanism was proposed to deal with the problem of relation overlapping. A pretrained transformer encoder was adopted to capture context information. Compared with the entity extraction pipeline, the constructed joint learning model was superior in recall, precision, and F1 measures, at 0.822, 0.825, and 0.818, respectively, 14% higher than the baseline model. The joint learning model could automatically extract features without any extra natural language processing tools. This is efficient in the disassembling of mixture symptom mentions. Furthermore, this superior performance at identifying overlapping relations could benefit the reassembling of separated symptom entities downstream. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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