Deep dynamic imputation of clinical time series for mortality prediction

Abstract Missing values in clinical time-series data are pervasive and inevitable; they not only increase the complexity and difficulty of analyzing the data but also lead to biased results. To tackle these two problems, researchers have been exploring recurrent neural network (RNN)-based methods for detecting how well missing values are addressed with the aim of achieving state-of-the-art performance. However, these methods have two practical drawbacks. 1) Handling time-series data with multiple, irregular, abnormal values is difficult. 2) The patterns that may be present in the missing clinical data are not thoroughly considered. Moreover, to the best of our knowledge, none of these methods have been explicitly designed to dynamically optimize the imputation quality for better performance in the realm of clinical time-series analytics. By considering the quality of imputed values , we propose a 2-step integrated imputation-prediction model based on gated recurrent units (GRUs) for medical prediction tasks. In the first step, the missing values are imputed using a sophisticated model based on a replenished GRU with a hidden state decay mechanism (RGRU-D), which is followed by evaluation through two additional layers. In the second step, the optimized imputed values are used to predict the risk of mortality in critical patients. Our model effectively supplies missing values for the masking, time interval , bursty, and cumulative missing rate variables within an integrated deep architecture. Extensive experiments on a real-world ICU dataset demonstrate that our model performs better than the compared methods in terms of the imputation quality and prediction accuracy.