TY - JOUR AU - Li, Haodi AU - Yang, Ming AU - Chen, Qingcai AU - Tang, Buzhou AU - Wang, Xiaolong AU - Yan, Jun PY - 2018 DA - 2018/07/23 TI - Chemical-induced disease extraction via recurrent piecewise convolutional neural networks JO - BMC Medical Informatics and Decision Making SP - 60 VL - 18 IS - 2 AB - 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. SN - 1472-6947 UR - https://doi.org/10.1186/s12911-018-0629-3 DO - 10.1186/s12911-018-0629-3 ID - Li2018 ER -