单位：[1]Software School North university of China Taiyuan, Shanxi[2]Research Institute of Information Technology Tsinghua University Beijing[3]Beijing Dfusion Co.Ltd. Beijing[4]Department of Pulmonary and Critical Care Medicine China-Japan Friendship Hospital[5]National Clinical Research Center for Respiratory Diseases[6]Institute of Respiratory Medicine, Chinese Academy of Medical Science Beijing
In order to realize automatic recognition and extraction of entities in unstructured medical texts, a model combining language model conditional random field algorithm (CRF) and Bi-directional Long Short-term Memory networks (BiLSTM) is proposed. We crawled 804 drug specifications for treating asthma from the Internet, and then quantized the normalized field of drug specification word by a vector as the input of the neural network. Compared with the traditional machine learning algorithm CRF model, the system accuracy, recall and F1 value are improved by 6.18%, 5.2% and 4.87%. This model is applicable to extract named entity information from drug specification.
基金：
Beijing Nature Science Foundation of ChinaBeijing Natural Science Foundation [Z160003]
语种：
外文
被引次数：
WOS：
第一作者：
第一作者单位：[1]Software School North university of China Taiyuan, Shanxi
通讯作者：
推荐引用方式(GB/T 7714)：
Li Wei-Yan,Song Wen-Ai,Jia Xin-Hong,et al.Drug Specification Named Entity Recognition base on BiLSTM-CRF Model[J].2019 IEEE 43RD ANNUAL COMPUTER SOFTWARE and APPLICATIONS CONFERENCE (COMPSAC), VOL 2.2019,429-433.doi:10.1109/COMPSAC.2019.10244.
APA：
Li, Wei-Yan,Song, Wen-Ai,Jia, Xin-Hong,Yang, Ji-Jiang,Wang, Qing...&Yang, Ting.(2019).Drug Specification Named Entity Recognition base on BiLSTM-CRF Model.2019 IEEE 43RD ANNUAL COMPUTER SOFTWARE and APPLICATIONS CONFERENCE (COMPSAC), VOL 2,,
MLA：
Li, Wei-Yan,et al."Drug Specification Named Entity Recognition base on BiLSTM-CRF Model".2019 IEEE 43RD ANNUAL COMPUTER SOFTWARE and APPLICATIONS CONFERENCE (COMPSAC), VOL 2 .(2019):429-433
