Abstract
Effective disease prediction based on electronic health records (EHR) is an important topic in health informatics. The current methods usually use common deep-learning models for disease prediction. However, it is difficult to fully learn the graphic encounter structure of EHR to improve prediction performance. Moreover, in prediction tasks, chronic kidney disease (CKD) has a poor prognosis due to excessive risk factors and complex comorbidities. Therefore, we propose a CKD onset prediction model called Graph Convolution Synthetic Transformer (GCST) based on EHR, using Fusion Attention Mechanism to solve these challenges. By modifying Transformer, GCST uses Factorized Dense Attention and Medical Local Attention to learn global and local attention, generating Synthetic Attention to learn the potential encounter structure and meaningful medical knowledge of EHR. In addition, we also propose a transfer learning strategy based on sample weighted correction to guide the prediction of GCST in specific low-resource EHR. We conduct sufficient experiments on three datasets to test the performance of GCST. Experiments show that GCST has significant improvement over state-of-the-art models.
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Acknowledgment
This work is supported by the Natural Science Foundation of China (62172351), 14th Five-Year Plan Civil Aerospace Pre-research Project of China (D020101), and the Fund of Prospective Layout of Scientific Research for Nanjing University of Aeronautics and Astronautics.
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Zhu, D. et al. (2023). Graph Convolution Synthetic Transformer for Chronic Kidney Disease Onset Prediction. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14178. Springer, Cham. https://doi.org/10.1007/978-3-031-46671-7_3
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