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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 2/2018

Open Access 01-07-2018 | Research

Chemical-induced disease extraction via recurrent piecewise convolutional neural networks

Authors: Haodi Li, Ming Yang, Qingcai Chen, Buzhou Tang, Xiaolong Wang, Jun Yan

Published in: BMC Medical Informatics and Decision Making | Special Issue 2/2018

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Abstract

Background

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.

Results

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.

Conclusions

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.
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Metadata
Title
Chemical-induced disease extraction via recurrent piecewise convolutional neural networks
Authors
Haodi Li
Ming Yang
Qingcai Chen
Buzhou Tang
Xiaolong Wang
Jun Yan
Publication date
01-07-2018
Publisher
BioMed Central
DOI
https://doi.org/10.1186/s12911-018-0629-3

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