Abstract:
The auxiliary disease diagnosis based on electronic medical records is of great significance, providing doctors with diagnostic advice and avoiding misdiagnosis. Existing...Show MoreMetadata
Abstract:
The auxiliary disease diagnosis based on electronic medical records is of great significance, providing doctors with diagnostic advice and avoiding misdiagnosis. Existing work on disease diagnosis mainly utilizes deep learning models to extract sequence information in electronic medical records, ignoring the interpretability of results and the structural knowledge, especially causal knowledge. In our work, we propose a multiscale label attention network based on abductive causal graph (MSLAN-ACG) to improve model accuracy and interpretability of results. First, we construct multiple encoders in the multiscale label attention network, which can extract n-gram segment information of different lengths for each disease. Meanwhile, to enhance the interpretability of results, we visualize the weight score of different segments for disease results. Second, we propose a disease representation method by defining an abductive causal graph and then using graph convolutional network for knowledge fusion on this graph. The information propagation based on abductive causal graph is consistent with the actual abductive reasoning process from symptoms to diseases, making the model more reasonable. The effectiveness of our model is demonstrated by achieving state-of-the-art results on MIMICIII-50 and ChineseEMR datasets.
Date of Conference: 06-08 December 2022
Date Added to IEEE Xplore: 02 January 2023
ISBN Information:
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- IEEE Keywords
- Index Terms
- Diagnosis Of Disease ,
- Attention Network ,
- Multi-scale Network ,
- Abductive ,
- Causal Graph ,
- Electronic Health Records ,
- Deep Learning Models ,
- Knowledge Of Structure ,
- Graph Convolutional Network ,
- Information Propagation ,
- Reasoning Process ,
- Convolutional Layers ,
- Weight Matrix ,
- Attention Mechanism ,
- Text Data ,
- Convolution Kernel ,
- China Hospital ,
- Residual Block ,
- Knowledge Representation ,
- Convolution Kernel Size ,
- Medical Entities ,
- Named Entity Recognition ,
- Representative Disease ,
- BERT Model ,
- Pre-trained Language Models ,
- Medical Text ,
- Multi-label Classification Task ,
- Misdiagnosis Rate ,
- Representation Of Entities
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Diagnosis Of Disease ,
- Attention Network ,
- Multi-scale Network ,
- Abductive ,
- Causal Graph ,
- Electronic Health Records ,
- Deep Learning Models ,
- Knowledge Of Structure ,
- Graph Convolutional Network ,
- Information Propagation ,
- Reasoning Process ,
- Convolutional Layers ,
- Weight Matrix ,
- Attention Mechanism ,
- Text Data ,
- Convolution Kernel ,
- China Hospital ,
- Residual Block ,
- Knowledge Representation ,
- Convolution Kernel Size ,
- Medical Entities ,
- Named Entity Recognition ,
- Representative Disease ,
- BERT Model ,
- Pre-trained Language Models ,
- Medical Text ,
- Multi-label Classification Task ,
- Misdiagnosis Rate ,
- Representation Of Entities
- Author Keywords