A Multitask VAE for Time Series Preprocessing and Prediction of Blood Glucose Level

TL;DR

This paper introduces a novel deep learning model combining a variational auto-encoder and recurrent VAE to improve preprocessing and prediction of blood glucose levels from time series data, reducing bias and enhancing accuracy.

Contribution

The proposed model uniquely integrates a VAE with a recurrent VAE to handle missing data and preserve temporal dynamics in blood glucose prediction.

Findings

Improved accuracy over existing methods

Effective handling of missing and abnormal data

Enhanced preservation of temporal information

Abstract

Data preprocessing is a critical part of time series data analysis. Data from connected medical devices often have missing or abnormal values during acquisition. Handling such situations requires additional assumptions and domain knowledge. This can be time-consuming, and can introduce a significant bias affecting predictive model accuracy and thus, medical interpretation. To overcome this issue, we propose a new deep learning model to mitigate the preprocessing assumptions. The model architecture relies on a variational auto-encoder (VAE) to produce a preprocessing latent space, and a recurrent VAE to preserve the temporal dynamics of the data. We demonstrate the effectiveness of such an architecture on telemonitoring data to forecast glucose-level of diabetic patients. Our results show an improvement in terms of accuracy with respect of existing state-of-the-art methods and architectures.