Dynamic Interpretable Change Point Detection for Physiological Data Analysis

Jennifer Yu, Tina Behrouzi, Kopal Garg, Anna Goldenberg, Sana Tonekaboni
Proceedings of the 3rd Machine Learning for Health Symposium, PMLR 225:636-649, 2023.

Abstract

Identifying change points (CPs) in time series is crucial to guide better decision-making in healthcare, and facilitating timely responses to potential risks or opportunities. In maternal health, monitoring health signals in pregnant women allows healthcare providers to promptly respond to complications like preeclampsia or enhance delivery time detection, improving overall maternal care. Existing Change Point Detection (CPD) methods often fail to generalize effectively due to diverse underlying changes that can cause a CP. We propose Ti me Va rying CPD (TiVaCPD), a change point detection method that captures different types of changes in the underlying distribution of multidimensional data. It combines a dynamic window MMD test with a graphical Lasso estimator of feature covariance to measure both changes in the joint distribution of the observations as well as changes in feature dynamics. TiVaCPD generates a unifying CP score by evaluating the relative similarity of the statistical tests. Additionally, TiVaCPD score enhances interpretability by offering insight into the underlying causes of CPs through a detailed analysis of feature dynamics, which is especially valuable in healthcare applications. We evaluate the performance of TiVaCPD on both simulated and real-world data, showing that it can outperform state-of-the-art methods. We further demonstrate the appliance of TiVaCPD in a pregnancy-related case study, showcasing the joint shifts in physiological signals that facilitate the detection of delivery time.

Cite this Paper

BibTeX
@InProceedings{pmlr-v225-yu23a, title = {Dynamic Interpretable Change Point Detection for Physiological Data Analysis}, author = {Yu, Jennifer and Behrouzi, Tina and Garg, Kopal and Goldenberg, Anna and Tonekaboni, Sana}, booktitle = {Proceedings of the 3rd Machine Learning for Health Symposium}, pages = {636--649}, year = {2023}, editor = {Hegselmann, Stefan and Parziale, Antonio and Shanmugam, Divya and Tang, Shengpu and Asiedu, Mercy Nyamewaa and Chang, Serina and Hartvigsen, Tom and Singh, Harvineet}, volume = {225}, series = {Proceedings of Machine Learning Research}, month = {10 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v225/yu23a/yu23a.pdf}, url = {https://proceedings.mlr.press/v225/yu23a.html}, abstract = {Identifying change points (CPs) in time series is crucial to guide better decision-making in healthcare, and facilitating timely responses to potential risks or opportunities. In maternal health, monitoring health signals in pregnant women allows healthcare providers to promptly respond to complications like preeclampsia or enhance delivery time detection, improving overall maternal care. Existing Change Point Detection (CPD) methods often fail to generalize effectively due to diverse underlying changes that can cause a CP. We propose Ti me Va rying CPD (TiVaCPD), a change point detection method that captures different types of changes in the underlying distribution of multidimensional data. It combines a dynamic window MMD test with a graphical Lasso estimator of feature covariance to measure both changes in the joint distribution of the observations as well as changes in feature dynamics. TiVaCPD generates a unifying CP score by evaluating the relative similarity of the statistical tests. Additionally, TiVaCPD score enhances interpretability by offering insight into the underlying causes of CPs through a detailed analysis of feature dynamics, which is especially valuable in healthcare applications. We evaluate the performance of TiVaCPD on both simulated and real-world data, showing that it can outperform state-of-the-art methods. We further demonstrate the appliance of TiVaCPD in a pregnancy-related case study, showcasing the joint shifts in physiological signals that facilitate the detection of delivery time.} }
Endnote
%0 Conference Paper %T Dynamic Interpretable Change Point Detection for Physiological Data Analysis %A Jennifer Yu %A Tina Behrouzi %A Kopal Garg %A Anna Goldenberg %A Sana Tonekaboni %B Proceedings of the 3rd Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2023 %E Stefan Hegselmann %E Antonio Parziale %E Divya Shanmugam %E Shengpu Tang %E Mercy Nyamewaa Asiedu %E Serina Chang %E Tom Hartvigsen %E Harvineet Singh %F pmlr-v225-yu23a %I PMLR %P 636--649 %U https://proceedings.mlr.press/v225/yu23a.html %V 225 %X Identifying change points (CPs) in time series is crucial to guide better decision-making in healthcare, and facilitating timely responses to potential risks or opportunities. In maternal health, monitoring health signals in pregnant women allows healthcare providers to promptly respond to complications like preeclampsia or enhance delivery time detection, improving overall maternal care. Existing Change Point Detection (CPD) methods often fail to generalize effectively due to diverse underlying changes that can cause a CP. We propose Ti me Va rying CPD (TiVaCPD), a change point detection method that captures different types of changes in the underlying distribution of multidimensional data. It combines a dynamic window MMD test with a graphical Lasso estimator of feature covariance to measure both changes in the joint distribution of the observations as well as changes in feature dynamics. TiVaCPD generates a unifying CP score by evaluating the relative similarity of the statistical tests. Additionally, TiVaCPD score enhances interpretability by offering insight into the underlying causes of CPs through a detailed analysis of feature dynamics, which is especially valuable in healthcare applications. We evaluate the performance of TiVaCPD on both simulated and real-world data, showing that it can outperform state-of-the-art methods. We further demonstrate the appliance of TiVaCPD in a pregnancy-related case study, showcasing the joint shifts in physiological signals that facilitate the detection of delivery time.
APA
Yu, J., Behrouzi, T., Garg, K., Goldenberg, A. & Tonekaboni, S.. (2023). Dynamic Interpretable Change Point Detection for Physiological Data Analysis. Proceedings of the 3rd Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 225:636-649 Available from https://proceedings.mlr.press/v225/yu23a.html.

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