Classifying medical relations in clinical text via convolutional neural networks

Abstract

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.

arXiv commentAccepted by Artificial Intelligence In Medicine

References in corpus (10)

• Efficient Estimation of Word Representations in Vector Space
• SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals
• End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
• Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation
• Classifying Relations by Ranking with Convolutional Neural Networks
• A Dependency-Based Neural Network for Relation Classification
• Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network
• Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling
• Local Translation Prediction with Global Sentence Representation
• Task-Oriented Learning of Word Embeddings for Semantic Relation Classification

Cited by in corpus (5)

• SECNLP: A Survey of Embeddings in Clinical Natural Language Processing
• Integrating Graph Contextualized Knowledge into Pre-trained Language Models
• A Survey of Knowledge Enhanced Pre-trained Models
• Rationalizing Medical Relation Prediction from Corpus-level Statistics
• REflex: Flexible Framework for Relation Extraction in Multiple Domains