UA-CRNN: Uncertainty-aware convolutional recurrent neural network for mortality risk prediction
Refereed conference paper presented and published in conference proceedings


摘要Accurate prediction of mortality risk is important for evaluating early treatments, detecting high-risk patients and improving healthcare outcomes. Predicting mortality risk from the irregular clinical time series data is challenging due to the varying time intervals in the consecutive records. Existing methods usually solve this issue by generating regular time series data from the original irregular data without considering the uncertainty in the generated data, caused by varying time intervals. In this paper, we propose a novel Uncertainty-Aware Convolutional Recurrent Neural Network (UA-CRNN), which incorporates the uncertainty information in the generated data to improve the mortality risk prediction performance. To handle the complex clinical time series data with sub-series of different frequencies, we propose to incorporate the uncertainty information into the sub-series level rather than the whole time series data. Specifically, we design a novel hierarchical uncertainty-aware decomposition layer (UADL) to adaptively decompose time series into different sub-series and assign them proper weights according to their reliabilities. Experimental results on two real-world clinical datasets demonstrate that the proposed UA-CRNN method significantly outperforms state-of-the-art methods in both short-term and long-term mortality risk predictions.
著者Tan Q., Ma A., Ye M., Yang B., Deng H., Wong V., Tse Y., Yip T., Wong G., Ching J., Chan F., Yuen P.
會議名稱CIKM '19: The 28th ACM International Conference on Information and Knowledge Management
會議論文集題名International Conference on Information and Knowledge Management, Proceedings
出版社Association for Computing Machinery
頁次109 - 118
關鍵詞Convolutional Recurrent Neural Network, Machine Learning, Uncertainty-Aware Prediction, Mortality Risk Prediction, Time Series Decomposition

上次更新時間 2020-11-08 於 00:47