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학술지 Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction
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저자
전은지, Ahmad Wisnu Mulyadi, 최재훈, 석흥일
발행일
202109
출처
IEEE Transactions on Neural Networks and Learning Systems, v.32 no.9, pp.4052-4062
ISSN
2162-237X
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TNNLS.2020.3016670
협약과제
20HR4400, 심혈관질환을 위한 인공지능 주치의 기술 개발, 김승환
초록
Electronic health records (EHRs) are characterized as nonstationary, heterogeneous, noisy, and sparse data; therefore, it is challenging to learn the regularities or patterns inherent within them. In particular, sparseness caused mostly by many missing values has attracted the attention of researchers who have attempted to find a better use of all available samples for determining the solution of a primary target task through defining a secondary imputation problem. Methodologically, existing methods, either deterministic or stochastic, have applied different assumptions to impute missing values. However, once the missing values are imputed, most existing methods do not consider the fidelity or confidence of the imputed values in the modeling of downstream tasks. Undoubtedly, an erroneous or improper imputation of missing variables can cause difficulties in the modeling as well as a degraded performance. In this study, we present a novel variational recurrent network that: 1) estimates the distribution of missing variables (e.g., the mean and variance) allowing to represent uncertainty in the imputed values; 2) updates hidden states by explicitly applying fidelity based on a variance of the imputed values during a recurrence (i.e., uncertainty propagation over time); and 3) predicts the possibility of in-hospital mortality. It is noteworthy that our model can conduct these procedures in a single stream and learn all network parameters jointly in an end-to-end manner. We validated the effectiveness of our method using the public data sets of MIMIC-III and PhysioNet challenge 2012 by comparing with and outperforming other state-of-the-art methods for mortality prediction considered in our experiments. In addition, we identified the behavior of the model that well represented the uncertainties for the imputed estimates, which showed a high correlation between the uncertainties and mean absolute error (MAE) scores for imputation.
키워드
Bioinformatics, deep generative model, deep learning (DL), electronic health records (EHRs), missing value imputation, mortality prediction, time series modeling, uncertainty
KSP 제안 키워드
Data sets, Deep generative model, End to End(E2E), In-hospital mortality, Mean Absolute Error, Missing value imputation, Network Parameters, Over time, Public Data, Recurrent network, Time series modeling