UA-CRNN: Uncertainty-aware convolutional recurrent neural network for mortality risk prediction

Qingxiong Tan, Andy Jinhua Ma, Mang Ye, Baoyao Yang, Huiqi Deng, Vincent Wai Sun Wong, Yee Kit Tse, Terry Cheuk Fung Yip, Grace Lai Hung Wong, Jessica Yuet Ling Ching, Francis Ka Leung Chan, Pong Chi YUEN*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationCIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages109-118
Number of pages10
ISBN (Electronic)9781450369763
DOIs
Publication statusPublished - 3 Nov 2019
Event28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
Duration: 3 Nov 20197 Nov 2019

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
Country/TerritoryChina
CityBeijing
Period3/11/197/11/19

Scopus Subject Areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

User-Defined Keywords

  • Convolutional Recurrent Neural Network
  • Machine Learning
  • Mortality Risk Prediction
  • Time Series Decomposition
  • Uncertainty-Aware Prediction

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