Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method.
@article{arxiv.1805.06665,
title = {Classifying medical relations in clinical text via convolutional neural networks},
author = {Bin He and Yi Guan and Rui Dai},
journal= {arXiv preprint arXiv:1805.06665},
year = {2018}
}