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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (5): 83-94    DOI: 10.11925/infotech.2096-3467.2020.1211
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Normalizing Chinese Disease Names with Multi-feature Fusion
Han Pu1,2(),Zhang Zhanpeng1,Zhang Mingtao1,Gu Liang1
1School of Management, Nanjing University of Posts & Telecommunications, Nanjing 210023, China;
2Jiangsu Provincial Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
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[Objective] This paper proposes a normalization model for Chinese disease names based on multi-feature fusion, aiming to address the issue of multiple alternative disease names for online health communities. [Methods] First, we constructed a normalized dataset for Chinese disease names used by online health communities. Second, we conducted experiments in Chinese and English with the LSTM, GRU and CNN models. Third, we generated external semantic feature vectors with Word2vec and GloVe. Finally, we developed the normalization model MFCF-CNN for Chinese disease names based on the multi-feature fusion and self-attention mechanism. [Results] We examined the proposed model with Accuracy @ 10 dataset. The accuracy of our MFCF-CNN model reached 85.48%, which is 8.84% higher than the basic CNN model. Our model made better use of global and local semantic features. [Limitations] The amount of the experiment data needs to be expanded. [Conclusions] The proposed model promotes the normalization of Chinese disease names, which benefits the medical knowledge graph construction and natural language understanding in Chinese.

Key wordsDisease Name Normalization      Supervised Learning      Convolutional Neural Network      Self-attention Mechanism     
Received: 04 December 2020      Published: 27 May 2021
ZTFLH:  G250  
Fund:*The work is supported by the National Social Science Fund of China(17CTQ022);the Jiangsu Graduate Research and Innovation Program Fund Project(KYCX20_0844)
Corresponding Authors: Han Pu     E-mail:

Cite this article:

Han Pu,Zhang Zhanpeng,Zhang Mingtao,Gu Liang. Normalizing Chinese Disease Names with Multi-feature Fusion. Data Analysis and Knowledge Discovery, 2021, 5(5): 83-94.

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Convolutional Neural Network Model
Experimental Flowchart
疾病名称 词级文本 字级文本
水痘 背部 腹部 水痘 感觉 瘙痒 水泡患者 局部 皮疹 轻微 疼痛 皮炎平 效果带状疱疹 疼痛感 涂抹 阿昔洛韦 软膏 配合 口服 胸腺肽 肠溶片 增强 免疫力 免疫 功能 低下 背 部 胸 腹 现 水 痘 感 觉 痒 瘙 泡 病 患 局 皮 疹 轻 微 疼 痛 抹 炎 平 效 果 状 疱 涂 昔 洛 韦 软 膏 配 合 口 服 腺 肽 肠 溶 片 增 强 免 疫 力 主 功 低
风湿热 湿热 出汗 畏寒 怕冷 特别 口腔溃疡 嗓子 痛发于 舌尖 唇部 牙龈 胀痛 口腔 异味 月经 病史 服药 过敏史 饮食 偏辣 高血压 高血糖 高血脂 冠心病 高尿酸 血症舌苔 湿 热 汗 畏 寒 冷 特 容 易 口 腔 溃 疡 嗓 子 痛 舌 尖 唇 部 牙 龈 胀 异 味 时 月 正 病 史 服 敏 饮 食 偏 辣 高 血 压 糖 脂 冠 心 尿 酸 症 苔
关节炎 血清 骨钙素 测定 胶原蛋白 序列 维生素 白介素 肿瘤 坏死 因子 日去 好坏 泼尼松 拍片 骨折 随访 减药 关系 血 清 骨 钙 素 测 B 胶 原 蛋 序 列 羟 维 生 D 介 肿 瘤 坏 死 子 日 泼 尼 松 龙 片 吃 拍 骨 折 样 访 减 药 关 系
Examples of Chinese Disease Dataset
Multi-feature Fusion Model MFCF-CNN Based on Self-attention Mechanism
Dataset Division and Model Training Process
领域 语料来源
Wiki-WCv 通用领域 维基百科2020版
EMR-WCv 临床医学领域 CCKS2017电子病历
MA-WCv 生物医学领域 万方医学网-医学文献摘要
OHC-WCv 在线医疗健康领域 好问康、求医问药网
External Semantic Features and Source of Corpus
疾病名称 疾病描述
Arthritis of knee arthritic knees
Lightheadedness light headed
Myalgia Muscle aches & pains
Taste sense altered taste perversion
Foot pain pain on the sole of my feet
Myositis muscle inflammation
Severe pain severe pain close to my the crotch area
Myalgia soreness of muscles
Examples of English Disease Dataset
输入句向量维度 100 100 100
卷积核的数量 4 / /
神经元 128 128 128
输入样本数 20 20 20
迭代次数 10 20 20
Dropout机制 0.5
Softmax层数 归一化疾病名称数
注意力机制 自注意力机制
Experimental Parameter Settings
模型 Accuracy@1 Accuracy@5 Accuracy@10
CNN-WRv-ADR 18.71% 47.09% 54.19%
LSTM-WRv-ADR 22.58% 45.81% 68.39%
GRU-WRv-ADR 20.65% 47.10% 65.81%
CNN-WRv-ASK 61.19% 78.10% 80.12%
LSTM-WRv-ASK 65.12% 79.76% 84.76%
GRU-WRv-ASK 66.79% 79.29% 85.12%
CNN-WRv-CDND 60.98% 74.89% 76.64%
LSTM-WRv-CDND 59.34% 72.43% 75.21%
GRU-WRv-CDND 58.97% 71.63% 74.28%
CNN-CRv-CDND 70.06% 83.09% 84.48%
Accuracy of Chinese and English Disease Name Normalization
语义特征 Accuracy@1 Accuracy@5 Accuracy@10
Wiki-WCv 70.30% 83.40% 84.99%
EMR-WCv 69.25% 82.27% 83.75%
MA-WCv 70.36% 83.41% 84.92%
OHC-WCv 70.21% 83.52% 84.90%
Accuracy of Inducing External Semantic Feature Vectors on CNN-WCv Model
模型 Accuracy@1 Accuracy@5 Accuracy@10
CNN-WCv 70.21% 83.52% 84.90%
CNN-GCv 69.62% 83.21% 84.51%
MFCF-CNN-AWCv 70.64% 83.87% 85.28%
MFCF-CNN-AGCv 70.22% 83.71% 85.06%
MFCF-CNN-AWGCv 71.05% 83.95% 85.48%
Accuracy of Chinese Disease Name Normalization Based on MFCF
Comparative Analysis of Experimental Result
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