Drug-drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths.

ABSTRACT:

Motivation

Adverse events resulting from drug-drug interactions (DDI) pose a serious health issue. The ability to automatically extract DDIs described in the biomedical literature could further efforts for ongoing pharmacovigilance. Most of neural networks-based methods typically focus on sentence sequence to identify these DDIs, however the shortest dependency path (SDP) between the two entities contains valuable syntactic and semantic information. Effectively exploiting such information may improve DDI extraction.

Results

In this article, we present a hierarchical recurrent neural networks (RNNs)-based method to integrate the SDP and sentence sequence for DDI extraction task. Firstly, the sentence sequence is divided into three subsequences. Then, the bottom RNNs model is

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC6030919 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature