Classifying medical relations in clinical text via convolutional neural networks

被引：45

作者：

He, Bin ^{[1
]}

Guan, Yi ^{[1
]}

Dai, Rui ^{[2
]}

机构：

[1] Harbin Inst Technol, Res Ctr Language Technol, Harbin, Heilongjiang, Peoples R China

[2] Harbin Inst Technol, Dept Math, Harbin, Heilongjiang, Peoples R China

来源：

ARTIFICIAL INTELLIGENCE IN MEDICINE | 2019年 / 93卷

关键词：

Relation classification; Clinical text; Convolutional neural network; Multi-pooling; EXTRACTION;

D O I：

10.1016/j.artmed.2018.05.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：43 / 49

页数：7

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