Classifying relations in clinical narratives using segment graph convolutional and recurrent neural networks (Seg-GCRNs)

By Yifu Li, Ran Jin, Yuan Luo. 2018

62 citations recorded by the source corpus.

We propose to use segment graph convolutional and recurrent neural networks (Seg-GCRNs), which use only word embedding and sentence syntactic dependencies, to classify relations from clinical notes without manual feature engineering. In this study, the relations between 2 medical concepts are classified by simultaneously learning representations of text segments in the context of sentence syntactic dependency: preceding, concept1, middle, concept2, and succeeding segments. Seg-GCRN was systematically evaluated on the i2b2/VA relation classification challenge datasets. Experiments show that Seg-GCRN attains state-of-the-art micro-averaged F-measure for all 3 relation categories: 0.692 for classifying medical treatment-problem relations, 0.827 for medical test-problem relations, and 0.741 for medical problem-medical problem relations. Comparison with the previous state-of-the-art segment convolutional neural network (Seg-CNN) suggests that adding syntactic dependency information helps refine medical word embedding and improves concept relation classification without manual feature engineering. Seg-GCRN can be trained efficiently for the i2b2/VA dataset on a GPU platform.

Subfields: Artificial Intelligence, Molecular Biology, Information Extraction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Philip S. Yu
2. Lichao Sun
3. Anita M. Preininger
4. Congying Xia
5. Hao Peng
6. Congyin Xia
7. Jianxin Li
8. Fei Wang

In Molecular Biology

1. Anita M. Preininger
2. Philip S. Yu
3. Jianxin Li
4. Fei Wang
5. Hao Peng
6. Congyin Xia
7. Renyu Yang
8. Qian Li

In Information Extraction

1. Philip S. Yu
2. Fei Wang
3. Hao Peng
4. Anita M. Preininger

Citations in the corpus

• A Survey on Text Classification: From Shallow to Deep Learning (2020, methodology): “With the increasing attention of graph neural networks (GNNs), GNN-based models obtain excellent performance by encoding syntactic structure of sentences on semantic role labeling task [160], relation classification task [161] and machine translation task [162].”
• AI in Health: State of the Art, Challenges, and Future Directions (2019, methodology): “They also proposed segmented CNN [41] and RNN [42] to process short clinical notes and achieved state-of-the-art performance on relation classification.”
• A Survey on Text Classification: From Traditional to Deep Learning (2020, methodology): “With the increasing attention of Graph Neural Networks (GNNs), GNN-based models [207, 208] obtain excellent performance by encoding syntactic structure of sentences on semantic role labeling task [209], relation classification task [210], and machine translation task [211].”