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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (7): 123-132    DOI: 10.11925/infotech.2096-3467.2018.1454
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Annotating Chinese E-Medical Record for Knowledge Discovery
Jiahui Hu,An Fang(),Wanqing Zhao,Chenliu Yang,Huiling Ren
Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
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Abstract  

[Objective] This paper studies the annotation method for Chinese electronic medical records, aiming to improve the processing of massive clinical texts and clinical knowledge discovery. [Methods] First, we proposed annotation method for Chinese e-medical records, and constructed a visual interactive platform. Then, based on the word and phrase features of these records, we identified the medical name entities with natural language processing and machine learning approaches. [Results] A total of 700 annotated records were obtained, and the overall F value of the Pipeline-based annotation method reached 0.8772, which was 32.9% higher than those based on the original medical records. [Limitations] Since the electronic medical record contains sensitive privacy information, this study was conducted with open dataset, and the corpus size was limited. [Conclusions] The Chinese electronic medical record annotation method and platform constructed in this study could effectively process clinical texts, and the association of medical knowledge.

Key wordsChinese Electronic Medical Record      Text Annotation      Natural Language Processing      Machine Learning      Knowledge Discovery     
Received: 24 December 2018      Published: 06 September 2019
:  TP391  
Corresponding Authors: An Fang     E-mail: fang.an@imicams.ac.cn

Cite this article:

Jiahui Hu,An Fang,Wanqing Zhao,Chenliu Yang,Huiling Ren. Annotating Chinese E-Medical Record for Knowledge Discovery. Data Analysis and Knowledge Discovery, 2019, 3(7): 123-132.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.1454     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I7/123

数据集 症状和体征 检查和
检验
治疗 疾病和
诊断
身体部位 合计
训练集 6 486 7 987 853 515 8 942 24 783
测试集 1 345 1 559 195 207 1 777 5 083
序号 编码 实体类别
1 B-SYMPTOM 症状和体征
2 E-SYMPTOM
3 M-SYMPTOM
4 S-SYMPTOM
5 B-CHECK 检查和检验
6 E-CHECK
7 M-CHECK
8 S-CHECK
9 B-TREATMENT 治疗
10 E-TREATMENT
11 M-TREATMENT
12 S-TREATMENT
13 B-DISEASE 疾病和诊断
14 E-DISEASE
15 M-DISEASE
16 S-DISEASE
17 B-BODY 身体部位
18 E-BODY
19 M-BODY
20 S-BODY
21 O 非医疗实体
症状和
体征
检查和
检验
治疗 疾病和
诊断
身体
部位
总体
P 0.9898 0.9554 0.9588 0.9703 0.9237 0.9531
R 0.9864 0.8233 0.9555 0.9515 0.9358 0.9138
F值 0.9881 0.8845 0.9571 0.9608 0.9297 0.9331
症状和体征 检查和检验 治疗 疾病和诊断 身体
部位
总体
P 0.9439 0.9091 0.7945 0.7772 0.8419 0.8860
R 0.9636 0.7505 0.5949 0.6908 0.8149 0.8210
F值 0.9536 0.8222 0.6804 0.7315 0.8281 0.8522
症状和
体征
检查和
检验
治疗 疾病和
诊断
身体部位
重合度 0.6148 0.3263 0.1181 0.1803 0.2423
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