Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models
Sibghat Ullah, Xu Zhao, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck · 2020
Clinical time series are known for irregular, highly-sporadic and strongly-complex structures and are consequently difficult to model by traditional state-space models. In this paper, we investigate the potential of applying variational recurrent neural networks (VRNNs) for forecasting clinical time series extracted from electronic health records (EHRs) of patients. Variational recurrent neural networks (VRNNs) combine recurrent neural networks (RNNs) and variational inference (VI) and are state-of-the-art methods to model highly-variable sequential data such as text, speech, time series and multimedia signals in a generative fashion. We propose to incorporate multiple correlated time series to improve the forecasting of VRNNs. The selection of these correlated time series is based on the similarity of the supplementary medical information e.g., disease diagnostics, ethnicity and age etc. between the patients. We evaluate the effectiveness of utilizing such supplementary information with root mean square error (RMSE), on clinical benchmark data-set "Medical Information Mart for Intensive Care (MIMIC III)" for multi-step-ahead prediction. We further perform subjective analysis to highlight the effects of the similarity of the supplementary medical information on individual temporal features e.g., Systolic Blood Pressure (SBP), Heart Rate (HR) etc. of the patients from the same data-set. Our results clearly show that incorporating the correlated time series based on the supplementary medical information can help improving the accuracy of the VRNNs for clinical time series forecasting.