基于知识增强的多视野表征学习辅助诊断方法

王好天,李鑫,关毅,杨洋,李雪,姜京池

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中文信息学报 ›› 2023, Vol. 37 ›› Issue (12) : 167-176.
自然语言处理应用

基于知识增强的多视野表征学习辅助诊断方法

  • 王好天1,李鑫1,关毅1,杨洋1,李雪1,姜京池2
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Multi-view Representation Learning Network Based on Knowledge Augmentation for Auxiliary Diagnosis

  • WANG Haotian1, LI Xin1, GUAN Yi1, YANG Yang1, LI Xue1, JIANG Jingchi2
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摘要

针对辅助诊断过程中病人所患疾病不单一,多种疾病之间存在内在关联,及长病历文本特征提取较为困难等问题,该文提出一种基于知识增强的多视野表征学习方法。该方法首先使用Bi-LSTM和注意力网络、医疗知识图融合、预训练模型分别从字符视野、实体视野、文档视野提取疾病表征,并通过融合多视野信息从长病历文本中准确抽取疾病诊断相关特征。而后建模疾病间内在关联关系,基于图神经网络方法进行知识融合以增强疾病表征,并实现疾病预测。该模型利用多视野表征学习与知识增强方法,提升了疾病预测的性能,通过结果可视化为模型提供了可解释性。在华为云杯评测数据上的实验表明,该方法优于其他基线方法,消融实验验证了该方法各模块的有效性。

Abstract

To model internal correlations between diseases and extract features from long medical records, we propose a multi-view representation learning network based on knowledge augmentation for auxiliary diagnosis. Firstly, the method combines the Bi-LSTM, the attention network, the medical knowledge graph, and the pre-trained models to extract disease representations from character view, entity view, and document view, respectively. Then, the features related to disease diagnosis are accurately extracted from the long medical record text by the fusion of multi-view information. Secondly, the internal correlation between diseases is modeled by knowledge fusion based on the graph neural network to enhance disease representation. Finally, the model uses multi-view representation learning and knowledge enhancement methods to predict disease. Experiments on Huawei Cloud evaluation dataset show that the model is superior to baseline methods, and ablation studies prove the effectiveness of each module in this method.

关键词

知识增强 / 多视野表征学习 / 辅助诊断 / 多标签分类

Key words

knowledge augmentation / multi-view representation learning / auxiliary diagnosis / multi-label classification

引用本文

导出引用
王好天,李鑫,关毅,杨洋,李雪,姜京池. 基于知识增强的多视野表征学习辅助诊断方法. 中文信息学报. 2023, 37(12): 167-176
WANG Haotian, LI Xin, GUAN Yi, YANG Yang, LI Xue, JIANG Jingchi. Multi-view Representation Learning Network Based on Knowledge Augmentation for Auxiliary Diagnosis. Journal of Chinese Information Processing. 2023, 37(12): 167-176

参考文献

[1] YANG Y,HUO H, JIANG J, et al. Clinical decision-making framework against over-testing based on modeling implicit evaluation criteria[J]. Journal of Biomedical Informatics, 2021, 119: 103823.
[2] 安震威,来雨轩,冯岩松. 面向法律文书的自然语言理解[J]. 中文信息学报, 2022, 36(8): 1-11.
[3] LEDLEY R S, LUSTED L B. Reasoning foundations of medical diagnosis: Symbolic logic, probability, and value theory aid our understanding of how physicians reason[J]. Science, 1959, 130(3366): 9-21.
[4] YANG Z, HUANG Y, JIANG Y, et al. Clinical assistant diagnosis for electronic medical record based on convolutional neural network[J]. Scientific Reports, 2018, 8(1): 1-9.
[5] MULLENBACH J, WIEGREFFE S, DUKE J, et al. Explainable prediction of medical codes from clinical text[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018: 1101-1111.
[6] LI F, YU H. ICD coding from clinical text using multi-filter residual convolutional neural network[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(05): 8180-8187.
[7] VU T, NGUYEN D Q, NGUYEN A. A label attention model foricd coding from clinical text[J]. arXiv preprint arXiv:2007.06351, 2020.
[8] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[J]. arXiv preprint arXiv:1609.02907, 2016.
[9] 刘勘,张雅荃. 基于医疗知识图谱的并发症辅助诊断[J]. 中文信息学报, 2020, 34(10): 85-93,104.
[10] ZHAO C, JIANG J, GUAN Y, et al. EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning[J]. Artificial Intelligence in Medicine, 2018, 87: 4.
[11] WANG H, GUAN Y, MA L, et al. Multi-scale label attention network based on abductive causal graph for disease diagnosis[C]//Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine. IEEE Computer Society, 2022: 2542-2549.
[12] XIE X, XIONG Y, YU P S, et al. Ehr coding with multi-scale feature attention and structured knowledge graph propagation[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019: 649-658.
[13] YUAN Q, CHEN J, LU C, et al. The graph-based mutual attentive network for automatic diagnosis[C]//Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence, 2021: 3393-3399.
[14] DEVLIN J, CHANG M W, LEE K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019: 4171-4186.
[15] SUN Y, WANG S, LI Y, et al. Ernie: Enhanced representation through knowledge integration[J].arXiv preprint arXiv:1904.09223, 2019.
[16] GU Y,TINN R, CHENG H, et al. Domain-specific language model pretraining for biomedical natural language processing[J]. ACM Transactions on Computing for Healthcare, 2021, 3(1): 1-23.
[17] HUANG C W, TSAI S C, Chen Y N. PLM-ICD: Automatic ICD coding with pretrained language models[J].arXiv preprint arXiv:2207.05289, 2022.
[18] LAMPLE G, BALLESTEROS M, SUBRAMANIAN S, et al. Neural architectures for named entity recognition[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016: 260-270.
[19] YOON KIM. Convolutional neural networks for sentence classification[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2016: 1746-1751.
[20] LIU P,QIU X, HUANG X. Recurrent neural network for text classification with multi-task learning[J]. arXiv preprint arXiv:1605.05101, 2016.
[21] LAI S, XU L, LIU K, et al. Recurrent convolutional neural networks for text classification[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence, 2015.
[22] WANG X, MERCER R E,RUDZICZ F. KenMeSH: Knowledge-enhanced end-to-end biomedical text labelling[J]. arXiv preprint arXiv:2203.06835, 2022.
[23] BELTAGY I, PETERS M E, COHAN A. Longformer: The long-document transformer[J]. arXiv preprint arXiv:2004.05150, 2020.

基金

科技创新2030——“新一代人工智能”重大项目(2021ZD0113302)
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