Forecasting time series in healthcare with Gaussian processes and Dynamic Time Warping based subset selection
- Author
- Chetanya Puri, Gerben Kooijman, Bart Vanrumste and Stijn Luca (UGent)
- Organization
- Abstract
- Modelling real-world time series can be challenging in the absence of sufficient data. Limited data in healthcare, can arise for several reasons, namely when the number of subjects is insufficient or the observed time series is irregularly sampled at a very low sampling frequency. This is especially true when attempting to develop personalised models, as there are typically few data points available for training from an individual subject. Furthermore, the need for early prediction (as is often the case in healthcare applications) amplifies the problem of limited availability of data. This article proposes a novel personalised technique that can be learned in the absence of sufficient data for early prediction in time series. Our novelty lies in the development of a subset selection approach to select time series that share temporal similarities with the time series of interest, commonly known as the test time series. Then, a Gaussian processes-based model is learned using the existing test data and the chosen subset to produce personalised predictions for the test subject. We will conduct experiments with univariate and multivariate data from real-world healthcare applications to show that our strategy outperforms the state-of-the-art by around 20%.
- Keywords
- Forecasting, Gaussian processes, machine learning, time series analysis
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8770089
- MLA
- Puri, Chetanya, et al. “Forecasting Time Series in Healthcare with Gaussian Processes and Dynamic Time Warping Based Subset Selection.” IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, vol. 26, no. 12, 2022, pp. 6126–37, doi:10.1109/jbhi.2022.3214343.
- APA
- Puri, C., Kooijman, G., Vanrumste, B., & Luca, S. (2022). Forecasting time series in healthcare with Gaussian processes and Dynamic Time Warping based subset selection. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 26(12), 6126–6137. https://doi.org/10.1109/jbhi.2022.3214343
- Chicago author-date
- Puri, Chetanya, Gerben Kooijman, Bart Vanrumste, and Stijn Luca. 2022. “Forecasting Time Series in Healthcare with Gaussian Processes and Dynamic Time Warping Based Subset Selection.” IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 26 (12): 6126–37. https://doi.org/10.1109/jbhi.2022.3214343.
- Chicago author-date (all authors)
- Puri, Chetanya, Gerben Kooijman, Bart Vanrumste, and Stijn Luca. 2022. “Forecasting Time Series in Healthcare with Gaussian Processes and Dynamic Time Warping Based Subset Selection.” IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 26 (12): 6126–6137. doi:10.1109/jbhi.2022.3214343.
- Vancouver
- 1.Puri C, Kooijman G, Vanrumste B, Luca S. Forecasting time series in healthcare with Gaussian processes and Dynamic Time Warping based subset selection. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS. 2022;26(12):6126–37.
- IEEE
- [1]C. Puri, G. Kooijman, B. Vanrumste, and S. Luca, “Forecasting time series in healthcare with Gaussian processes and Dynamic Time Warping based subset selection,” IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, vol. 26, no. 12, pp. 6126–6137, 2022.
@article{8770089,
abstract = {{Modelling real-world time series can be challenging in the absence of sufficient data. Limited data in healthcare, can arise for several reasons, namely when the number of subjects is insufficient or the observed time series is irregularly sampled at a very low sampling frequency. This is especially true when attempting to develop personalised models, as there are typically few data points available for training from an individual subject. Furthermore, the need for early prediction (as is often the case in healthcare applications) amplifies the problem of limited availability of data. This article proposes a novel personalised technique that can be learned in the absence of sufficient data for early prediction in time series. Our novelty lies in the development of a subset selection approach to select time series that share temporal similarities with the time series of interest, commonly known as the test time series. Then, a Gaussian processes-based model is learned using the existing test data and the chosen subset to produce personalised predictions for the test subject. We will conduct experiments with univariate and multivariate data from real-world healthcare applications to show that our strategy outperforms the state-of-the-art by around 20%.}},
author = {{Puri, Chetanya and Kooijman, Gerben and Vanrumste, Bart and Luca, Stijn}},
issn = {{2168-2194}},
journal = {{IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS}},
keywords = {{Forecasting,Gaussian processes,machine learning,time series analysis}},
language = {{eng}},
number = {{12}},
pages = {{6126--6137}},
title = {{Forecasting time series in healthcare with Gaussian processes and Dynamic Time Warping based subset selection}},
url = {{http://doi.org/10.1109/jbhi.2022.3214343}},
volume = {{26}},
year = {{2022}},
}
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