TY - GEN
T1 - HiTANet
T2 - 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
AU - Luo, Junyu
AU - Ye, Muchao
AU - Xiao, Cao
AU - Ma, Fenglong
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/8/23
Y1 - 2020/8/23
N2 - Deep learning methods especially recurrent neural network based models have demonstrated early success in disease risk prediction on longitudinal patient data. Existing works follow a strong assumption to implicitly assume the stationary disease progression during each time period, and thus, take a homogeneous way to decay the information from previous time steps for all patients. However,in reality, disease progression is non-stationary. Besides, the key time steps for a target disease vary among patients. To leverage time information for risk prediction in a more reasonable way, we propose a new hierarchical time-aware attention network, named HiTANet, which imitates the decision making process of doctors inrisk prediction. Particularly, HiTANet models time information in local and global stages. The local evaluation stage has a time aware Transformer that embeds time information into visit-level embed-ding and generates local attention weight for each visit. The global synthesis stage further adopts a time-aware key-query attention mechanism to assign global weights to different time steps. Finally, the two types of attention weights are dynamically combined to generate the patient representations for further risk prediction. We evaluate HiTANet on three real-world datasets. Compared with the best results among twelve competing baselines, HiTANet achieves over 7% in terms of F1 score on all datasets, which demonstrates the effectiveness of the proposed model and the necessity of modeling time information in risk prediction task.
AB - Deep learning methods especially recurrent neural network based models have demonstrated early success in disease risk prediction on longitudinal patient data. Existing works follow a strong assumption to implicitly assume the stationary disease progression during each time period, and thus, take a homogeneous way to decay the information from previous time steps for all patients. However,in reality, disease progression is non-stationary. Besides, the key time steps for a target disease vary among patients. To leverage time information for risk prediction in a more reasonable way, we propose a new hierarchical time-aware attention network, named HiTANet, which imitates the decision making process of doctors inrisk prediction. Particularly, HiTANet models time information in local and global stages. The local evaluation stage has a time aware Transformer that embeds time information into visit-level embed-ding and generates local attention weight for each visit. The global synthesis stage further adopts a time-aware key-query attention mechanism to assign global weights to different time steps. Finally, the two types of attention weights are dynamically combined to generate the patient representations for further risk prediction. We evaluate HiTANet on three real-world datasets. Compared with the best results among twelve competing baselines, HiTANet achieves over 7% in terms of F1 score on all datasets, which demonstrates the effectiveness of the proposed model and the necessity of modeling time information in risk prediction task.
UR - http://www.scopus.com/inward/record.url?scp=85090413454&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090413454&partnerID=8YFLogxK
U2 - 10.1145/3394486.3403107
DO - 10.1145/3394486.3403107
M3 - Conference contribution
AN - SCOPUS:85090413454
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 647
EP - 656
BT - KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 23 August 2020 through 27 August 2020
ER -