@inproceedings{40835fbbec7546cd9349f2bffaa5ce64,
title = "Convolutional Gated Recurrent Units for Medical Relation Classification",
abstract = "Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have become the mainstream architectures for relation classification. We propose a unified architecture, which exploits the advantages of CNN and RNN simultaneously, to identify medical relations in clinical records, with only word embedding features. Our model learns phrase-level features through a CNN layer, and these feature representations are directly fed into a bidirectional gated recurrent unit (GRU) layer to capture long-term feature dependencies. We evaluate our model on two clinical datasets, and experiments demonstrate that our model performs better than previous single-model methods on both datasets.",
keywords = "clinical record, convolutional neural network, gated recurrent unit, relation classification",
author = "Bin He and Yi Guan and Rui Dai",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",
year = "2019",
month = jan,
day = "21",
doi = "10.1109/BIBM.2018.8621228",
language = "英语",
series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "646--650",
editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",
booktitle = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",
address = "美国",
}