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

arXiv: https://arxiv.org/abs/2410.00015 · HTML: https://mlsanity.com/paper/2410.00015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 14 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [A Recurrent Latent Variable Model for Sequential Data](https://mlsanity.com/paper/1506.02216/) (2015)
- [Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series](https://mlsanity.com/paper/2003.00662/) (2020)
- [Learning Insulin-Glucose Dynamics in the Wild](https://mlsanity.com/paper/2008.02852/) (2020)
- [An Ensemble Learning Approach for Exercise Detection in Type 1 Diabetes Patients](https://mlsanity.com/paper/2305.10353/) (2023)

## Abstract (arXiv)

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.