MTSSP: Missing Value Imputation in Multivariate Time Series for Survival Prediction

In recent years, there has been a lot of research on deep learning for survival prediction in EHR (Electronic Health Record). At present, EHR usually contains multivariate time series data with missing values. How to better predict mortality based on such data is what many studies are currently doing. Most of the mortality prediction methods based on deep learning only pay attention to missing value filling or adjusting the model structure to enhance the mortality prediction performance, but they do not combine these two aspects well. In this paper, we propose MTSSP (Multivariate Time Series for Survival Prediction), a new method that combines missing value filling and time series classification. It takes two representations of the loss pattern: the mask and the time interval, which are combined into the recurrent neural network for the interpolation of the missing value of patient characteristics. When the missing data values are interpolated, the model combines bidirectional RNN and one-dimensional CNN to jointly capture the patient's medical behavior from a global and local perspective to enhance the representation ability of data information, thereby improving the prediction accuracy of the model. In the end, we conducted our mortality prediction experiments on the real-world emergency MIMIC-III dataset and MIMIC-IV dataset. The experimental results demonstrate that the proposed approach has been shown to significantly outperform other approaches.