Comparative study of clustering models for multivariate time series from connected medical devices
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913191194263552 |
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| author | Courrier, Violaine Biernacki, Christophe Preda, Cristian Vittrant, Benjamin |
| author_facet | Courrier, Violaine Biernacki, Christophe Preda, Cristian Vittrant, Benjamin |
| contents | 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 DGM${}^2$ which allows the group affiliation of an individual to change over time (dynamic clustering). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17286 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Comparative study of clustering models for multivariate time series from connected medical devices Courrier, Violaine Biernacki, Christophe Preda, Cristian Vittrant, Benjamin Machine Learning Artificial Intelligence Neural and Evolutionary Computing 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 DGM${}^2$ which allows the group affiliation of an individual to change over time (dynamic clustering). |
| title | Comparative study of clustering models for multivariate time series from connected medical devices |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2312.17286 |