Clinical Time Series Imputation using Conditional Information Bottleneck

Clinical time series imputation presents a significant challenge because it requires capturing the underlying temporal dynamics from partially observed time series data input. Among the recent successes of imputation methods based on generative models, the information bottleneck (IB) framework offers a well-suited theoretical foundation for multiple imputations, allowing us to account for the uncertainty associated with the imputed values. However, direct application of IB framework to time series data without considering temporal context can lead to a substantial loss of temporal dependencies. To address such a challenge, we propose a novel conditional information bottleneck (CIB) approach for time series imputation, which aims to mitigate the potentially negative consequences of the regularization constraint by reducing the redundant information conditioned on the temporal context. Our experiments, conducted on real-world healthcare dataset and image sequences, demonstrate that our method significantly improves imputation performance, and also enhances prediction performance based on the imputed values.