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

TL;DR

This paper introduces PhysioMTL, a novel personalized multi-task learning framework using optimal transport to accurately model individual HRV patterns, accounting for stressors and heterogeneity in wearable data.

Contribution

We develop PhysioMTL, integrating optimal transport with multi-task learning to personalize HRV analysis and improve predictions on unseen subjects using limited data.

Findings

Outperforms existing methods on synthetic and real datasets.

Accurately predicts HRV for new subjects with only 20% of data.

Enables counterfactual analysis of stress effects on HRV.

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.