Chemical-induced disease extraction via recurrent piecewise convolutional neural networks

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

doi:10.1186/s12911-018-0629-3

Abstract

BackgroundExtracting 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.ResultsExperimental 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.ConclusionsA 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.

Highlights

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
To avoid fussy feature engineering, deep learning methods were applied to chemical-induced disease (CID) extraction [9], including convolutional neural networks (CNN) [10] and long short term memory neural networks (LSTM) [11]
Take the baselien system as an example, when the domain knowledge is added, the system’s F-score is improved by 15.72% (52.92% vs 68.64%). Both the piecewise strategy and attention mechanism are beneficial to the CNN-based systems and they are complementary to each other

Summary

Introduction

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. To avoid fussy feature engineering, deep learning methods were applied to CID extraction [9], including convolutional neural networks (CNN) [10] and long short term memory neural networks (LSTM) [11]. In these systems, domain knowledge about adverse drug reactions, and some new techniques, such as piecewise strategy [12] and attention mechanism [13], widely used in other domains are not considered. Gu [15] improved the CNN model by adding syntactic information of cross-sentence, and the performance has been further improved All these methods extract chemical-disease relations from single sentences or adjacent sentences. It should be noted that this paper is an extension of our previous paper [14]

Methods

Results

Conclusion