Classifying medical relations in clinical text via convolutional neural networks

05/17/2018
by   Bin He, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/29/2018

Convolutional Gated Recurrent Units for Medical Relation Classification

Convolutional neural network (CNN) and recurrent neural network (RNN) mo...
research
05/02/2020

Rationalizing Medical Relation Prediction from Corpus-level Statistics

Nowadays, the interpretability of machine learning models is becoming in...
research
10/15/2020

Where's the Question? A Multi-channel Deep Convolutional Neural Network for Question Identification in Textual Data

In most clinical practice settings, there is no rigorous reviewing of th...
research
04/24/2015

Classifying Relations by Ranking with Convolutional Neural Networks

Relation classification is an important semantic processing task for whi...
research
06/30/2016

Relation extraction from clinical texts using domain invariant convolutional neural network

In recent years extracting relevant information from biomedical and clin...
research
08/05/2015

Relation Classification via Recurrent Neural Network

Deep learning has gained much success in sentence-level relation classif...
research
01/07/2020

Multimodal Semantic Transfer from Text to Image. Fine-Grained Image Classification by Distributional Semantics

In the last years, image classification processes like neural networks i...

Please sign up or login with your details

Forgot password? Click here to reset