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Chemical-induced disease extraction via recurrent piecewise convolutional neural networks.
- Source :
-
BMC Medical Informatics & Decision Making . 7/23/2018 Supplement2, Vol. 18 Issue 2, pN.PAG-N.PAG. 1p. 1 Diagram, 4 Charts. - Publication Year :
- 2018
-
Abstract
- <bold>Background: </bold>Extracting relationships between chemicals and diseases from unstructured literature have attracted plenty of attention since the relationships are very useful for a large number of biomedical applications such as drug repositioning and pharmacovigilance. A number of machine learning methods have been proposed for chemical-induced disease (CID) extraction due to some publicly available annotated corpora. Most of them suffer from time-consuming feature engineering except deep learning methods. In this paper, we propose a novel document-level deep learning method, called recurrent piecewise convolutional neural networks (RPCNN), for CID extraction.<bold>Results: </bold>Experimental results on a benchmark dataset, the CDR (Chemical-induced Disease Relation) dataset of the BioCreative V challenge for CID extraction show that the highest precision, recall and F-score of our RPCNN-based CID extraction system are 65.24, 77.21 and 70.77%, which is competitive with other state-of-the-art systems.<bold>Conclusions: </bold>A novel deep learning method is proposed for document-level CID extraction, where domain knowledge, piecewise strategy, attention mechanism, and multi-instance learning are combined together. The effectiveness of the method is proved by experiments conducted on a benchmark dataset. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14726947
- Volume :
- 18
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- BMC Medical Informatics & Decision Making
- Publication Type :
- Academic Journal
- Accession number :
- 130871054
- Full Text :
- https://doi.org/10.1186/s12911-018-0629-3