Research Area: Machine Learning
Specific entity terms such as disease, test, symptom, and genes in Electronic Medical Record (EMR) can be extracted by Named Entity Recognition (NER). However, limited resources of labeled EMR pose a great challenge for mining medical entity terms. In this study, a novel multitask bi-directional RNN model combined with deep transfer learning is proposed as a potential solution of transferring knowledge and data augmentation to enhance NER performance with limited data. The proposed model has been evaluated using micro average F-score, macro average F-score and accuracy. It is observed that the proposed model outperforms the baseline model in the case of discharge datasets. For instance, for the case of discharge summary, the micro average F-score is improved by 2.55% and the overall accuracy is improved by 7.53%. For the case of progress notes, the micro average F-score and the overall accuracy are improved by 1.63% and 5.63%, respectively.
Keywords:
Deep Learning
Named Entity Recognition
Chinese Electronic Medical Records
Deep Transfer Learning
Lstm
Rnn
Machine Learning
Author(s) Name: Xishuang Dong ,Shanta Chowdhury,Lijun Qian,Xiangfang Li,Yi Guan,Jinfeng Yang,Qiubin Yu
Journal name: PLOS One
Conferrence name:
Publisher name: PLOS
DOI: 10.1371/journal.pone.0216046
Volume Information:
