Research on entity relationship extraction of Chinese medical literature and application in diabetes medical literature.
10.7507/1001-5515.202001009
- Author:
Zhiyuan FAN
1
;
Xuan HE
1
;
Pin LIANG
1
;
Jing LU
1
;
Yan KANG
2
Author Information
1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, P.R.China.
2. College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, Guangdong 518118, P.R.China.
- Publication Type:Journal Article
- Keywords:
convolutional neural network;
diabetes;
entity relationship extraction;
knowledge graph;
medical literature
- MeSH:
Algorithms;
China;
Diabetes Mellitus;
Electronic Health Records;
Humans;
Neural Networks, Computer
- From:
Journal of Biomedical Engineering
2021;38(3):563-573
- CountryChina
- Language:Chinese
-
Abstract:
The medical literature contains a wealth of valuable medical knowledge. At present, the research on extraction of entity relationship in medical literature has made great progress, but with the exponential increase in the number of medical literature, the annotation of medical text has become a big problem. In order to solve the problem of manual annotation time such as consuming and heavy workload, a remote monitoring annotation method is proposed, but this method will introduce a lot of noise. In this paper, a novel neural network structure based on convolutional neural network is proposed, which can solve a large number of noise problems. The model can use the multi-window convolutional neural network to automatically extract sentence features. After the sentence vectors are obtained, the sentences that are effective to the real relationship are selected through the attention mechanism. In particular, an entity type (ET) embedding method is proposed for relationship classification by adding entity type characteristics. The attention mechanism at sentence level is proposed for relation extraction in allusion to the unavoidable labeling errors in training texts. We conducted an experiment using 968 medical references on diabetes, and the results showed that compared with the baseline model, the present model achieved good results in the medical literature, and F1-score reached 93.15%. Finally, the extracted 11 types of relationships were stored as triples, and these triples were used to create a medical map of complex relationships with 33 347 nodes and 43 686 relationship edges. Experimental results show that the algorithm used in this paper is superior to the optimal reference system for relationship extraction.
