Proceedings chapter
Open access

Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus

Presented at Online and Punta Cana, Dominican Republic, 7-11 November 2021
PublisherAssociation for Computational Linguistics (ACL)
Publication date2021-11-07
First online date2021-10-19

We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of hierarchical documents using the permutation invariant message passing operations, we show that we can gain extra performance improvements using our proposed selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents. We applied our model to classify clinical trial (CT) protocols into completed and terminated categories. We use bag-of-words based, as well as pre-trained transformer-based embeddings to featurize the graph nodes, achieving f1-scores around 0.85 on a publicly available large scale CT registry of around 360K protocols. We further demonstrate how the selective pooling can add insights into the CT termination status prediction. We make the source code and dataset splits accessible.

  • Natural Language Processing
  • Graph Neural Networks
  • Deep Learning
  • Clinical Trials
Citation (ISO format)
FERDOWSI, Sohrab et al. Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP). Online and Punta Cana, Dominican Republic. [s.l.] : Association for Computational Linguistics (ACL), 2021. p. 608–618. doi: 10.18653/v1/2021.emnlp-main.48
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