Localization model of traditional Chinese medicine Zang-fu based on ALBERT and Bi-GRU
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摘要: 臟腑定位，即明確病變所在的臟腑，是中醫臟腑辨證的重要階段。本文旨在通過神經網絡模型搭建中醫臟腑定位模型，輸入癥狀文本信息，輸出對應的病變臟腑標簽，為實現中醫輔助診療的臟腑辨證提供支持。將中醫的臟腑定位問題建模為自然語言處理中的多標簽文本分類問題，基于中醫的醫案數據，提出一種基于預訓練模型ALBERT和雙向門控循環單元（Bi-GRU）的臟腑定位模型。對比實驗和消融實驗的結果表明，本文提出的方法在中醫臟腑定位的問題上相比于多層感知機模型、決策樹模型具有更高的準確性，與Word2Vec文本表示方法相比，本文使用的ALBERT預訓練模型的文本表示方法有效提升了模型的準確率。在模型參數上，ALBERT預訓練模型相比BERT模型降低了模型參數量，有效減小了模型大小。最終，本文提出的臟腑定位模型在測試集上F1值達到了0.8013。Abstract: The rapid development of artificial intelligence (AI) has injected new vitality into various industries and provided new ideas for the development of traditional Chinese medicine (TCM). The combination of AI and TCM provides more technical support for TCM auxiliary diagnosis and treatment. In the history of TCM, many methods of syndrome differentiation have been observed, among which the differentiation of Zang-fu organs is one of the important methods. The purpose of this paper is to provide support for the localization of Zang-fu in TCM through AI technology. Localization of Zang-fu organs is a method of determining the location of lesions in such organs and is an important stage in the differentiation of Zang-fu organs in TCM. In this paper, the localization model of TCM Zang-fu organs through the neural network model was established. Through the input of symptom text information, the corresponding Zang-fu label for a lesion could 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 was abstracted as multi-label text classification in natural language processing. Using the medical record data of TCM, a Zang-fu localization model based on pretraining models a lite BERT (ALBERT) and bidirectional gated recurrent unit (Bi-GRU) was proposed. Comparison and ablation experiments finally show that the proposed method is more accurate than multilayer perceptron and the decision tree. Moreover, using an ALBERT pretraining model for text representation effectively improves the accuracy of the localization model. In terms of model parameters, the ALBERT pretraining model greatly reduces the number of model parameters compared with the BERT model and effectively reduces the model size. Finally, the F1-value of the Zang-fu localization model proposed in this paper reaches 0.8013 on the test set, which provided certain support for the TCM auxiliary diagnosis and treatment.
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表 1 臟腑定位數據格式
Table 1. Zang-fu location data format
No. Symptoms Tag 1 Legs ache, and wake up unable to sleep, along with hemoptysis and a sore throat spleen, kidney, heart 2 The patient had high blood pressure, weakness in the right limb, and pain in the left upper arm liver, kidney 
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表 2 訓練過程中的參數
Table 2. Parameters in the training process
Parameter name Parameter value Max_seq_lenth 128 GRU_units 128 Dropout 0.4 Learning_rate 1×10?4 Epochs 10 Batch_size 128
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表 3 多標簽分類對比實驗結果
Table 3. Comparative experimental results of multiple label classification
No. Method Precision Recall F1-value 1 Word2Vec+Bi-GRU 0.8015 0.7653 0.7830 2 MLP Classifier 0.7091 0.7067 0.7079 3 Decision Tree Classifier 0.6744 0.6633 0.6688 4 ALBERT+Bi-GRU 0.8301 0.7745 0.8013
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表 4 BERT與ALBERT對比實驗結果
Table 4. Comparative experimental results of BERT and ALBERT
Id Method Precision Recall F1-value Time/s Model_
parameters/
MB1 BERT+Bi-GRU 0.8253 0.7783 0.8011 99.8219 363.3 2 ALBERT+Bi-GRU 0.8301 0.7745 0.8013 84.7045 37.3
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表 5 多標簽分類消融實驗結果
Table 5. Ablation experiment multiple label classification results
Method Precision Recall F1-value ALBERT 0.7711 0.7315 0.7508 ALBERT+Bi-GRU 0.8301 0.7745 0.8013
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