Traditional Chinese Medicine Zang-fu Localization Model based on ALBERT and Bi-GRU
-
摘要: 脏腑定位,即明确病变所在的脏腑,是中医脏腑辨证的重要阶段。本文旨在通过神经网络模型搭建中医脏腑定位模型,通过输入症状文本信息,输出对应的病变脏腑标签,为实现中医辅助诊疗的脏腑辨证提供支持。本文将中医的脏腑定位问题建模为自然语言处理中的多标签文本分类问题,基于中医的医案数据,提出一种基于预训练模型ALBERT和双向GRU(门控循环单元)的脏腑定位模型。本文提出的模型最终在测试集上F1值达到了0.8013。进行对比实验和消融实验后,实验结果表明本文提出的方法在中医脏腑定位的问题上相比于多层感知机模型、决策树模型具有更高的准确性,以及使用ALBERT预训练模型进行文本表示相比使用Word2Vec方法有效提升了模型的准确率。Abstract: Localization of zang-fu organs is a method to determine the location of lesions in zang-fu organs. It is an important stage of differentiation of zang-fu organs in traditional Chinese medicine(TCM). The purpose of this paper is to establish the localization model of TCM zang-fu organs through the neural network model. Through the input of symptom text information, the corresponding lesion zang-fu label can be output to provide support for the realization of zang-fu syndrome differentiation in TCM assisted diagnosis and treatment. In this paper, the localization of zang-fu organs is abstracted as multi-label text classification in natural language processing. Based on the medical record data of traditional Chinese medicine, a zang-fu localization model based on pre-training model ALBERT(A Lite BERT) and Bi-GRU (bidirectional gated circulation unit) was proposed. The F1-value of this model reached 0.8013 on the test set. The comparison and ablation experiments finally show that the proposed method is more accurate than multilayer perceptron and decision tree. And using ALBERT pre-training model for text representation effectively improves the accuracy of the model.
-

计量
- 文章访问数: 84
- HTML全文浏览量: 39
- 被引次数: 0