External Knowledge-Based Weakly Supervised Learning Approach on Chinese Clinical Named Entity Recognition

2020 ◽  
pp. 336-352
Author(s):  
Yeheng Duan ◽  
Long-Long Ma ◽  
Xianpei Han ◽  
Le Sun ◽  
Bin Dong ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Learning Approach ◽  
Weakly Supervised Learning ◽  
External Knowledge ◽  
Named Entity ◽  
Knowledge Based ◽  
Weakly Supervised

scholarly journals A Weakly-Supervised Named Entity Recognition Machine Learning Approach for Emergency Medical Services Clinical Audit

2021 ◽  
Vol 18 (15) ◽  
pp. 7776
Author(s):  
Han Wang ◽  
Wesley Lok Kin Yeung ◽  
Qin Xiang Ng ◽  
Angeline Tung ◽  
Joey Ai Meng Tay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Medical Services ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Learning Approach ◽  
Named Entity ◽  
Emergency Medical ◽  
Machine Learning Approach ◽  
Weakly Supervised ◽  
Clinical Audits

Clinical performance audits are routinely performed in Emergency Medical Services (EMS) to ensure adherence to treatment protocols, to identify individual areas of weakness for remediation, and to discover systemic deficiencies to guide the development of the training syllabus. At present, these audits are performed by manual chart review, which is time-consuming and laborious. In this paper, we report a weakly-supervised machine learning approach to train a named entity recognition model that can be used for automatic EMS clinical audits. The dataset used in this study contained 58,898 unlabeled ambulance incidents encountered by the Singapore Civil Defence Force from 1st April 2019 to 30th June 2019. With only 5% labeled data, we successfully trained three different models to perform the NER task, achieving F1 scores of around 0.981 under entity type matching evaluation and around 0.976 under strict evaluation. The BiLSTM-CRF model was 1~2 orders of magnitude lighter and faster than our BERT-based models. Our proposed proof-of-concept approach may improve the efficiency of clinical audits and can also help with EMS database research. Further external validation of this approach is needed.

When External Knowledge Does Not Aggregate in Named Entity Recognition

2021 ◽  
pp. 616-627
Author(s):  
Pedro Ivo Monteiro Privatto ◽  
Ivan Rizzo Guilherme
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
External Knowledge ◽  
Named Entity
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