Research on the construction of drug knowledge base based on machine learning
10.3760/cma.j.cn111325-20210104-00003
- VernacularTitle:基于机器学习的药品知识库构建研究
- Author:
Yunfei HOU
1
;
Yicheng LI
;
Zongyu ZOU
;
Zijun ZHOU
Author Information
1. 北京大学公共卫生学院卫生政策与管理学系 100191
- Keywords:
Knowledge bases;
Named entity recognition;
Entity standardization;
Drug;
Machine learning
- From:
Chinese Journal of Hospital Administration
2021;37(3):232-236
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a drug knowledge base based on drug instructions.Methods:Six hundred randomly selected drug instructions were labeled manually and divided into training set and test set. The training was based on bidirectional long short-term memory(Bi-LSTM) and conditional random fields(CRF) model to complete the recognition of medical entities. The extracted entities were standardized by the hybrid model of " similarity calculation and rule mapping table" , and then the drug information was imported into the Access database.Results:In the task of named entity recognition based on Bi-LSTM and CRF model, except for the crowd entities, the other entities had achieved good results with an F-value higher than 85%. Based on the hybrid model of " similarity calculation and rule mapping table" , the accuracy of entity standardization was 88.23%.Conclusions:The effect of the machine learning model in this study is similar to that of other named entity recognition and entity standardization studies, which can complete the task of drug knowledge base construction satisfactorily.
