Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (2): 411-417.DOI: 10.11772/j.issn.1001-9081.2023030260
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Kaitian WANG1, Qing YE1,2(), Chunlei CHENG1,2
Received:
2023-03-16
Revised:
2023-05-25
Accepted:
2023-05-26
Online:
2023-07-05
Published:
2024-02-10
Contact:
Qing YE
About author:
WANG Kaitian, born in 1999, M. S. candidate. His research interests include natural language processing, data mining.Supported by:
通讯作者:
叶青
作者简介:
王楷天(1999—),男,黑龙江牡丹江人,硕士研究生,主要研究方向:自然语言处理、数据挖掘基金资助:
CLC Number:
Kaitian WANG, Qing YE, Chunlei CHENG. Classification method for traditional Chinese medicine electronic medical records based on heterogeneous graph representation[J]. Journal of Computer Applications, 2024, 44(2): 411-417.
王楷天, 叶青, 程春雷. 基于异构图表示的中医电子病历分类方法[J]. 《计算机应用》唯一官方网站, 2024, 44(2): 411-417.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023030260
信息类别 | 内容 |
---|---|
性别 | 女 |
望诊 | 面色偏滞;形体稍胖,神情平静,语速偏快、话多 |
脉诊 | 脉略滑,右脉边界欠清,左关稍旺,左寸上略浮 |
舌诊 | 舌质偏暗青,苔淡黄稍厚 |
查体 | 咽壁滤泡,分泌物多 |
主诉 | 右耳鸣半月余 |
中医诊断 | 感冒 |
Tab. 1 Example of TCM electronic medical records
信息类别 | 内容 |
---|---|
性别 | 女 |
望诊 | 面色偏滞;形体稍胖,神情平静,语速偏快、话多 |
脉诊 | 脉略滑,右脉边界欠清,左关稍旺,左寸上略浮 |
舌诊 | 舌质偏暗青,苔淡黄稍厚 |
查体 | 咽壁滤泡,分泌物多 |
主诉 | 右耳鸣半月余 |
中医诊断 | 感冒 |
真实情况 | 预测情况 | |
---|---|---|
预测为该疾病 | 预测不为该疾病 | |
预测准确 | TP | TN |
预测错误 | FP | FN |
Tab. 2 Confusion matrix
真实情况 | 预测情况 | |
---|---|---|
预测为该疾病 | 预测不为该疾病 | |
预测准确 | TP | TN |
预测错误 | FP | FN |
参数名 | 符号 | 值 |
---|---|---|
GCN学习率 | gcn_lr | 0.05 |
GCN权重衰减 | gcn_weight_decay | 10-5 |
GCN隐藏层特征维度 | n_hidden | 32 |
PMI阈值 | p | 0 |
LERT学习率 | lert_lr | 10-5 |
LERT权重衰减 | lert_weight_decay | 10-4 |
batch大小 | batch_size | 128 |
权重 | λ | 0.5 |
迭代次数 | epoch | 200 |
Tab. 3 Super parameter setting
参数名 | 符号 | 值 |
---|---|---|
GCN学习率 | gcn_lr | 0.05 |
GCN权重衰减 | gcn_weight_decay | 10-5 |
GCN隐藏层特征维度 | n_hidden | 32 |
PMI阈值 | p | 0 |
LERT学习率 | lert_lr | 10-5 |
LERT权重衰减 | lert_weight_decay | 10-4 |
batch大小 | batch_size | 128 |
权重 | λ | 0.5 |
迭代次数 | epoch | 200 |
模型 | 精确率 | 召回率 | F1 | AUC |
---|---|---|---|---|
LSTM | 0.666 5 | 0.664 8 | 0.663 1 | 0.882 9 |
Text-GCN | 0.749 5 | 0.750 9 | 0.748 3 | 0.930 7 |
LERT | 0.767 4 | 0.763 6 | 0.763 0 | 0.915 2 |
Text_CNN | 0.724 0 | 0.723 6 | 0.721 2 | 0.924 6 |
Text_RNN | 0.690 8 | 0.685 5 | 0.684 9 | 0.915 9 |
FastText | 0.725 0 | 0.714 5 | 0.713 4 | 0.937 3 |
TCM-GCN | 0.784 6 | 0.781 8 | 0.780 7 | 0.927 2 |
Tab. 4 Result comparison of different models
模型 | 精确率 | 召回率 | F1 | AUC |
---|---|---|---|---|
LSTM | 0.666 5 | 0.664 8 | 0.663 1 | 0.882 9 |
Text-GCN | 0.749 5 | 0.750 9 | 0.748 3 | 0.930 7 |
LERT | 0.767 4 | 0.763 6 | 0.763 0 | 0.915 2 |
Text_CNN | 0.724 0 | 0.723 6 | 0.721 2 | 0.924 6 |
Text_RNN | 0.690 8 | 0.685 5 | 0.684 9 | 0.915 9 |
FastText | 0.725 0 | 0.714 5 | 0.713 4 | 0.937 3 |
TCM-GCN | 0.784 6 | 0.781 8 | 0.780 7 | 0.927 2 |
实验 | 构图方法 | 精确率 | 召回率 | F1 | AUC |
---|---|---|---|---|---|
1 | LERT+one-hot+PMI | 0.771 5 | 0.770 9 | 0.770 0 | 0.916 2 |
2 | LERT+ BW25 | 0.763 4 | 0.761 8 | 0.760 5 | 0.906 2 |
3 | BW25+PMI | 0.767 1 | 0.765 5 | 0.764 3 | 0.913 3 |
4 | LERT+TF-IDF+PMI | 0.774 1 | 0.772 7 | 0.771 4 | 0.916 2 |
5 | LERT+BW25+PMI | 0.784 6 | 0.781 8 | 0.780 7 | 0.927 2 |
Tab. 5 Results of ablation experiments
实验 | 构图方法 | 精确率 | 召回率 | F1 | AUC |
---|---|---|---|---|---|
1 | LERT+one-hot+PMI | 0.771 5 | 0.770 9 | 0.770 0 | 0.916 2 |
2 | LERT+ BW25 | 0.763 4 | 0.761 8 | 0.760 5 | 0.906 2 |
3 | BW25+PMI | 0.767 1 | 0.765 5 | 0.764 3 | 0.913 3 |
4 | LERT+TF-IDF+PMI | 0.774 1 | 0.772 7 | 0.771 4 | 0.916 2 |
5 | LERT+BW25+PMI | 0.784 6 | 0.781 8 | 0.780 7 | 0.927 2 |
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