Exploring two biomedical text genres for disease recognition

Applications of Machine Learning in Biomedical Text Processing and Food Industry

Machine Learning for Healthcare Applications ◽

10.1002/9781119792611.ch10 ◽

2021 ◽

pp. 151-167

Author(s):

K. Paramesha ◽

H.L. Gururaj ◽

Om Prakash Jena

Keyword(s):

Machine Learning ◽

Food Industry ◽

Text Processing ◽

Biomedical Text ◽

Applications Of Machine Learning

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GHS-NET A Generic Hybridized Shallow Neural Network for Multi-Label Biomedical Text Classification

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2021.103699 ◽

2021 ◽

pp. 103699

Author(s):

Muhammad Ali Ibrahim ◽

Muhammad Usman Ghani Khan ◽

Faiza Mehmood ◽

Muhammad Nabeel Asim ◽

Waqar Mahmood

Keyword(s):

Neural Network ◽

Text Classification ◽

Biomedical Text ◽

Biomedical Text Classification

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Biomedical text similarity evaluation using attention mechanism and siamese neural network

IEEE Access ◽

10.1109/access.2021.3099021 ◽

2021 ◽

pp. 1-1

Author(s):

Z.G. Li ◽

H. Chen ◽

H.Y. Chen

Keyword(s):

Neural Network ◽

Attention Mechanism ◽

Biomedical Text ◽

Text Similarity

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Extraction of causal relations based on SBEL and BERT model

Database ◽

10.1093/database/baab005 ◽

2021 ◽

Vol 2021 ◽

Author(s):

Yifan Shao ◽

Haoru Li ◽

Jinghang Gu ◽

Longhua Qian ◽

Guodong Zhou

Keyword(s):

State Of The Art ◽

Causal Relation ◽

Relation Extraction ◽

The Other ◽

Biomedical Text ◽

Intermediate Form ◽

Biomedical Text Mining ◽

Causal Relations ◽

The One ◽

Stage 1

Abstract Extraction of causal relations between biomedical entities in the form of Biological Expression Language (BEL) poses a new challenge to the community of biomedical text mining due to the complexity of BEL statements. We propose a simplified form of BEL statements [Simplified Biological Expression Language (SBEL)] to facilitate BEL extraction and employ BERT (Bidirectional Encoder Representation from Transformers) to improve the performance of causal relation extraction (RE). On the one hand, BEL statement extraction is transformed into the extraction of an intermediate form—SBEL statement, which is then further decomposed into two subtasks: entity RE and entity function detection. On the other hand, we use a powerful pretrained BERT model to both extract entity relations and detect entity functions, aiming to improve the performance of two subtasks. Entity relations and functions are then combined into SBEL statements and finally merged into BEL statements. Experimental results on the BioCreative-V Track 4 corpus demonstrate that our method achieves the state-of-the-art performance in BEL statement extraction with F1 scores of 54.8% in Stage 2 evaluation and of 30.1% in Stage 1 evaluation, respectively. Database URL: https://github.com/grapeff/SBEL_datasets

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