Proceedings

A Novel Sequence-to-Subgraph Framework for Diagnosis Classification

Jun Chen, Quan Yuan, Chao Lu, Haifeng Huang

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3606-3612.

https://doi.org/10.24963/ijcai.2021/496

Keywords

  • Natural Language Processing: NLP Applications and Tools
  • Machine Learning Applications: Bio/Medicine
  • Multidisciplinary Topics and Applications: Biology and Medicine

Abstract

Text-based diagnosis classification is a critical problem in AI-enabled healthcare studies, which assists clinicians in making correct decision and lowering the rate of diagnostic errors. Previous studies follow the routine of sequence based deep learning models in NLP literature to deal with clinical notes. However, recent studies find that structural information is important in clinical contents that greatly impacts the predictions. In this paper, a novel sequence-to-subgraph framework is introduced to process clinical texts for classification, which changes the paradigm of managing texts. Moreover, a new classification model under the framework is proposed that incorporates subgraph convolutional network and hierarchical diagnostic attentive network to extract the layered structural features of clinical texts. The evaluation conducted on both the real-world English and Chinese datasets shows that the proposed method outperforms the state-of-the-art deep learning based diagnosis classification models.

Jun Chen, Quan Yuan, Chao Lu, and Haifeng Huang. 2021. A Novel Sequence-to-Subgraph Framework for Diagnosis Classification. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3606–3612. https://doi.org/10.24963/ijcai.2021/496
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