# Comparative study of clustering models for multivariate time series from connected medical devices

arXiv: https://arxiv.org/abs/2312.17286 · HTML: https://mlsanity.com/paper/2312.17286/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series](https://mlsanity.com/paper/2103.02164/) (2021)
- [MAGMA: Inference and Prediction with Multi-Task Gaussian Processes](https://mlsanity.com/paper/2007.10731/) (2020)
- [Cluster-Specific Predictions with Multi-Task Gaussian Processes](https://mlsanity.com/paper/2011.07866/) (2020)

## Abstract (arXiv)

In healthcare, patient data is often collected as multivariate time series, providing a comprehensive view of a patient's health status over time. While this data can be sparse, connected devices may enhance its frequency. The goal is to create patient profiles from these time series. In the absence of labels, a predictive model can be used to predict future values while forming a latent cluster space, evaluated based on predictive performance. We compare two models on Withing's datasets, M AGMAC LUST which clusters entire time series and DGM2 which allows the group affiliation of an individual to change over time (dynamic clustering).