JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2024, Vol. 59 ›› Issue (7): 122-130.doi: 10.6040/j.issn.1671-9352.0.2023.291

• Review • Previous Articles    

Chinese disease text classification model driven by medical knowledge

Chao LI(),Wei LIAO*()   

  1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
  • Received:2023-06-30 Online:2024-07-20 Published:2024-07-15
  • Contact: Wei LIAO E-mail:2057775195@qq.com;liaowei54@126.com

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Abstract:

This study proposes a Chinese disease text classification model that integrates knowledge graph. Firstly, by introducing structured knowledge from external medical knowledge graph, a knowledge enhanced disease text vector representation is obtained; Secondly, the global semantic features and local semantic features of the disease text are extracted by using bidirectional long short-term memory network and convolutional neural network respectively. At the same time, the joint attention mechanism improves the efficiency of the model in extracting effective features information; Finally, the extracted features are concatenated and fused, and a classifier is used to output the classification result. The experimental results on the Chinese disease text dataset show that the proposed model has a classification accuracy, recall, and the harmonic mean value F1 of 95.21%, 95.64%, and 95.42%, respectively, which shows better classification performance compared to other models.

Key words: disease text classification, knowledge graph, CNN, BiLSTM, attention mechanism

CLC Number: 

  • TP391

Fig.1

DKCDM model"

Table 1

Disease text and corresponding department"

疾病文本 科室
医生您好，乙肝表面抗原阴性，谷丙转氨酶169，谷草转氨酶87正常吗？ 肝病科
  我周围有很多认识的人得这种病，有的人把甲状腺切除了，有的人症状比较轻，但是人也变得消瘦了。引起这种病的原因是什么，治疗方法是什么？ 内分泌科
前几天运动场有人跌倒后突然癫痫，全身发颤，请问癫痫是怎么造成的？ 神经科

Fig.2

LSTM unit structure"

Fig.3

BiLSTM model structure"

Table 2

Confusion matrix"

预测标签 Positive Negative
Positive TP FP
Negative FN TN

Fig.4

Confusing matrix of classification results"

Fig.5

Comparison of F1values between different models"

Table 3

Models test experimental results  单位: %"

模型 P R F1
SVM 90.32 90.64 90.48
TextCNN 92.17 92.43 92.30
TextRNN 92.25 92.68 92.46
FastText 93.72 93.54 93.47
TextRCNN 93.47 93.85 93.66
DKCDM 95.21 95.64 95.42

Table 4

Ablation experiment  单位: %"

模型 P R F1
Remove KG 93.62 93.96 93.79
Remove TransE 95.18 95.27 95.22
RemoveBiLSTM_Attention 94.76 94.12 94.44
Remove CNN_Attention 94.17 94.51 94.34
DKCDM 95.21 95.64 95.42
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