Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation

TL;DR

Bayes-CATSI introduces a Bayesian deep learning framework for medical time series imputation, enhancing uncertainty quantification and outperforming previous models in accuracy, especially on EEG, EOG, EMG, and EKG data.

Contribution

It integrates variational Bayesian inference into the CATSI model, enabling uncertainty quantification and improved imputation performance in medical time series data.

Findings

Bayes-CATSI outperforms CATSI by 9.57% in imputation accuracy.

The framework effectively quantifies uncertainty in predictions.

Open-source implementation is provided for broader application.

Abstract

Medical time series datasets feature missing values that need data imputation methods, however, conventional machine learning models fall short due to a lack of uncertainty quantification in predictions. Among these models, the CATSI (Context-Aware Time Series Imputation) stands out for its effectiveness by incorporating a context vector into the imputation process, capturing the global dependencies of each patient. In this paper, we propose a Bayesian Context-Aware Time Series Imputation (Bayes-CATSI) framework which leverages uncertainty quantification offered by variational inference. We consider the time series derived from electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), electrocardiology (EKG). Variational Inference assumes the shape of the posterior distribution and through minimization of the Kullback-Leibler(KL) divergence it finds variational densities that are closest to the true posterior distribution. Thus , we integrate the variational Bayesian deep learning layers into the CATSI model. Our results show that Bayes-CATSI not only provides uncertainty quantification but also achieves superior imputation performance compared to the CATSI model. Specifically, an instance of Bayes-CATSI outperforms CATSI by 9.57 %. We provide an open-source code implementation for applying Bayes-CATSI to other medical data imputation problems.