Abstract
Due to potential applications in chronic disease management and personalized healthcare, the EHRs data analysis has attracted much attentions of both researchers and practitioners. There are three main challenges in modeling longitudinal and heterogeneous EHRs data: heterogeneity, irregular temporality and interpretability. A series of deep learning methods have made remarkable progress in resolving these challenges. Nevertheless, most of existing attention models rely on capturing the 1-order temporal dependencies or 2-order multimodal relationships among feature elements. In this paper, we propose a time-guided high-order attention (TGHOA) model. The proposed method has three major advantages. (1) It can model longitudinal heterogeneous EHRs data via capturing the 3-order correlations of different modalities and the irregular temporal impact of historical events. (2) It can be used to identify the potential concerns of medical features to explain the reasoning process of healthcare model. (3) It can be easily expanded into cases with more modalities and flexibly applied in different prediction tasks. We evaluate the proposed method in two tasks of mortality prediction and disease ranking on two real world EHRs datasets. Extensive experimental results show the effectiveness of the proposed model.
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Acknowledgments
This work was supported in part by National Key Research and Development Program of China (No. 2017YFB1002804), National Natural Science Foundation of China (No. 61702511, 61720106006, 1711530243, 61620106003, 61432019, 61632007, U1705262, U1836220) and Key Research Program of Frontier Sciences, CAS, Grant NO. QYZDJSSWJSC039. This work was also supported by Research Program of National Laboratory of Pattern Recognition (No. Z-2018007) and CCF-Tencent Open Fund.
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Huang, Y., Yang, X., Xu, C. (2019). Time-Guided High-Order Attention Model of Longitudinal Heterogeneous Healthcare Data. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11670. Springer, Cham. https://doi.org/10.1007/978-3-030-29908-8_5
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