PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression

Jiacheng Zhu, Gregory Darnell, Agni Kumar, Ding Zhao, Bo Li, Xuanlong Nguyen, Shirley You Ren

Research output: Contribution to journalConference articlepeer-review

Abstract

Heart rate variability (HRV) is a practical and noninvasive measure of autonomic nervous system activity, which plays an essential role in cardiovascular health. However, using HRV to assess physiology status is challenging. Even in clinical settings, HRV is sensitive to acute stressors such as physical activity, mental stress, hydration, alcohol, and sleep. Wearable devices provide convenient HRV measurements, but the irregularity of measurements and uncaptured stressors can bias conventional analytical methods. To better interpret HRV measurements for downstream healthcare applications, we learn a personalized diurnal rhythm as an accurate physiological indicator for each individual. We develop Physiological Multitask-Learning (PhysioMTL) by harnessing Optimal Transport theory within a Multitask-learning (MTL) framework. The proposed method learns an individual-specific predictive model from heterogeneous observations, and enables estimation of an optimal transport map that yields a push forward operation onto the demographic features for each task. Our model outperforms competing MTL methodologies on unobserved predictive tasks for synthetic and two real-world datasets. Specifically, our method provides remarkable prediction results on unseen held-out subjects given only 20% of the subjects in real-world observational studies. Furthermore, our model enables a counterfactual engine that generates the effect of acute stressors and chronic conditions on HRV rhythms.

Original languageEnglish (US)
Pages (from-to)354-374
Number of pages21
JournalProceedings of Machine Learning Research
Volume174
StatePublished - 2022
Event3rd Conference on Health, Inference, and Learning, CHIL 2022 - Virtual, Online
Duration: Apr 7 2022Apr 8 2022

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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