Chemical-induced disease extraction via recurrent piecewise convolutional neural networks

BMC Medical Informatics and Decision Making

Xiaolong Wang¹  Qingcai Chen¹  Haodi Li¹  Buzhou Tang¹  Ming Yang²  Jun Yan³ 

[1] Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology;Pharmacy Department, Shenzhen Second People’s Hospital, First Affiliated Hospital of Shenzhen University;Yidu Cloud (Beijing) Technology Co., Ltd;

关键词: Chemical-induced disease;    Relation extraction;    Deep learning;    Convolutional neural network;   

DOI  :  10.1186/s12911-018-0629-3

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown