融合知识图谱的多通道中医辨证模型
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江西中医药大学

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中图分类号:

TP391

基金项目:

国家重点研发计划课题(2019YFC1712301); 江西省教育厅科技技术研究重点项目(GJJ201204);江西省教育厅科学技术研究项目(GJJ170727);江西中医药大学博士启动基金(2018WBZR021); 江西省一流学科建设科研启动基金专项项目(JXSYLXK-ZHYI059)


Multi-channel Chinese Medicine Syndrome Differentiation Model Integrating Knowledge Graph
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Affiliation:

Jiangxi University of Chinese Medicine

Fund Project:

National Key Research and Development Project (2019YFC1712301); Jiangxi Provincial Department of Education Science and Technology Research Key Project (GJJ201204); Jiangxi Provincial Department of Education Science and Technology Research Project (GJJ170727); Jiangxi University of Traditional Chinese Medicine Doctoral Funding (2018WBZR021); Jiangxi Province's first-class discipline construction Special Project of Research Startup Fund (JXSYLXK-ZHYI059)

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    摘要:

    中医辨证是中医临床立法、处方、用药的基础和前提。中医电子病历高质量语料缺乏,模型训练容易欠拟合,且四诊信息的症状表达形式存在较大差异,限制了网络模型对复杂症状的识别能力。针对上述问题,通过对四诊信息多通道的分开处理,以及人工构建的小规模知识图谱对模型训练进行知识的增强,提出了融合知识图谱的多通道中医辨证模型。实验结果表明,基于中医电子病历数据集,提出的模型在P@1指标、P@3指标、P@5指标上相比基线模型分别提高3.51%、3.38%、3.32%,相比其他网络结构模型也有不同程度的提高,验证了所提模型对中医辨证具有显著效果。

    Abstract:

    Chinese Medicine syndrome differentiation is the basis and premise of Chinese Medicine clinical legislation, prescription and medication. Due to the lack of high-quality corpus of electronic medical records of Chinese Medicine, the model training is prone to underfitting, and the symptom expression forms of the four diagnostic information are quite different, which limits the network model's ability to recognize complex symptoms. Aiming at the above problems, a multi-channel Chinese Medicine syndrome differentiation model integrating knowledge graphs is proposed by separately processing the multi-channels of the four diagnostic information and enhancing the knowledge of the model training by artificially constructed small-scale knowledge graphs. The experimental results show that, based on the Chinese Medicine electronic medical record data set, the proposed model is 3.51%, 3.38%, and 3.32% higher than the baseline model in terms of P@1 index, P@3 index, and P@5 index, respectively, compared with other network structures. The model has also been improved to varying degrees, which verifies that the proposed model has a significant effect on Chinese Medicine syndrome differentiation.

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引用本文

叶青,张素华,程春雷,等. 融合知识图谱的多通道中医辨证模型[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-11-15
  • 最后修改日期:2022-03-31
  • 录用日期:2022-04-30
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