Knowledge Commons of Institute of Automation,CAS
Time-Guided High-Order Attention Model of Longitudinal Heterogeneous Healthcare Data | |
Huang, Yi1,2; Yang, Xiaoshan1,2; Xu, Changsheng1,2 | |
2019-08 | |
会议名称 | 16th Pacific Rim International Conference on Artificial Intelligence |
会议日期 | 2019-8-26至2019-8-30 |
会议地点 | Cuvu, Yanuca Island, Fiji |
摘要 | 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. |
收录类别 | EI |
资助项目 | Research Program of National Laboratory of Pattern Recognition[Z-2018007] ; Key Research Program of Frontier Sciences, CAS[QYZDJSSWJSC039] ; National Natural Science Foundation of China[U1836220] ; National Natural Science Foundation of China[61632007] ; National Natural Science Foundation of China[61711530243] ; National Natural Science Foundation of China[61702511] ; National Key RD Plan of China[2017YFB1002804] ; National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[61432019] ; National Natural Science Foundation of China[61432019] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[61620106003] ; National Key RD Plan of China[2017YFB1002804] ; National Natural Science Foundation of China[61702511] ; National Natural Science Foundation of China[61711530243] ; National Natural Science Foundation of China[61632007] ; National Natural Science Foundation of China[U1836220] ; Key Research Program of Frontier Sciences, CAS[QYZDJSSWJSC039] ; Research Program of National Laboratory of Pattern Recognition[Z-2018007] |
语种 | 英语 |
七大方向——子方向分类 | 人工智能+医疗 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39213 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 |
通讯作者 | Xu, Changsheng |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Huang, Yi,Yang, Xiaoshan,Xu, Changsheng. Time-Guided High-Order Attention Model of Longitudinal Heterogeneous Healthcare Data[C],2019. |
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