A comparative study of biomedical named entity recognition methods based machine learning approach

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.

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A Comparative Study of Dictionary-based and Machine Learning-based Named Entity Recognition in Pashto

Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval ◽

10.1145/3443279.3443307 ◽

2020 ◽

Author(s):

Rafiullah Momand ◽

Shakirullah Waseeb ◽

Ahmad Masood Latif Rai

Keyword(s):

Machine Learning ◽

Comparative Study ◽

Named Entity Recognition ◽

Entity Recognition ◽

Named Entity

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Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks

BioMed Research International ◽

10.1155/2014/240403 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 49

Author(s):

Buzhou Tang ◽

Hongxin Cao ◽

Xiaolong Wang ◽

Qingcai Chen ◽

Hua Xu

Keyword(s):

Machine Learning ◽

Language Processing ◽

Named Entity Recognition ◽

Entity Recognition ◽

Biomedical Domain ◽

Crucial Step ◽

Named Entity ◽

Different Types ◽

Word Representation ◽

Biomedical Named Entity Recognition

Biomedical Named Entity Recognition (BNER), which extracts important entities such as genes and proteins, is a crucial step of natural language processing in the biomedical domain. Various machine learning-based approaches have been applied to BNER tasks and showed good performance. In this paper, we systematically investigated three different types of word representation (WR) features for BNER, including clustering-based representation, distributional representation, and word embeddings. We selected one algorithm from each of the three types of WR features and applied them to the JNLPBA and BioCreAtIvE II BNER tasks. Our results showed that all the three WR algorithms were beneficial to machine learning-based BNER systems. Moreover, combining these different types of WR features further improved BNER performance, indicating that they are complementary to each other. By combining all the three types of WR features, the improvements inF-measure on the BioCreAtIvE II GM and JNLPBA corpora were 3.75% and 1.39%, respectively, when compared with the systems using baseline features. To the best of our knowledge, this is the first study to systematically evaluate the effect of three different types of WR features for BNER tasks.

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