# Classifying medical relations in clinical text via convolutional neural networks

arXiv: https://arxiv.org/abs/1805.06665 · HTML: https://mlsanity.com/paper/1805.06665/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2018.

## Built on by (most cited first, 2 of 2)

- [Hero-Gang Neural Model For Named Entity Recognition](https://mlsanity.com/paper/2205.07177/) (2022)
- [Advancements in eHealth Data Analytics through Natural Language Processing and Deep Learning](https://mlsanity.com/paper/2401.10850/) (2024)

## Abstract (arXiv)

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.