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

arXiv: https://arxiv.org/abs/2203.12595 · HTML: https://mlsanity.com/paper/2203.12595/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 84 references that are in the map.
- First posted on arXiv 19 Mar 2022.

## Builds on (most cited first, 24 of 24)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Optimal transport mapping via input convex neural networks](https://mlsanity.com/paper/1908.10962/) (2019)
- [Large-Scale Optimal Transport and Mapping Estimation](https://mlsanity.com/paper/1711.02283/) (2017)
- [Plugin Estimation of Smooth Optimal Transport Maps](https://mlsanity.com/paper/2107.12364/) (2021)
- [Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation](https://mlsanity.com/paper/2106.09017/) (2021)
- [Towards Optimal Transport with Global Invariances](https://mlsanity.com/paper/1806.09277/) (2018)
- [A Principled Approach for Learning Task Similarity in Multitask Learning](https://mlsanity.com/paper/1903.09109/) (2019)
- [An Empirical Framework for Domain Generalization in Clinical Settings](https://mlsanity.com/paper/2103.11163/) (2021)
- [Self-supervised transfer learning of physiological representations from free-living wearable data](https://mlsanity.com/paper/2011.12121/) (2020)
- [Inverse optimal transport](https://mlsanity.com/paper/1905.03950/) (2019)
- [Optimal transport for causal discovery](https://mlsanity.com/paper/2201.09366/) (2022)
- [Wasserstein regularization for sparse multi-task regression](https://mlsanity.com/paper/1805.07833/) (2018)
- [Efficient and Scalable Multi-task Regression on Massive Number of Tasks](https://mlsanity.com/paper/1811.05695/) (2018)
- [Multi-subject MEG/EEG source imaging with sparse multi-task regression](https://mlsanity.com/paper/1910.01914/) (2019)
- [Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning](https://mlsanity.com/paper/2006.12777/) (2020)
- [SISTA: learning optimal transport costs under sparsity constraints](https://mlsanity.com/paper/2009.08564/) (2020)
- [Functional optimal transport: map estimation and domain adaptation for functional data](https://mlsanity.com/paper/2102.03895/) (2021)
- [Estimating Respiratory Rate From Breath Audio Obtained Through Wearable Microphones](https://mlsanity.com/paper/2107.14028/) (2021)
- [Graph-Driven Generative Models for Heterogeneous Multi-Task Learning](https://mlsanity.com/paper/1911.08709/) (2019)
- [Towards Interpretable Multi-Task Learning Using Bilevel Programming](https://mlsanity.com/paper/2009.05483/) (2020)
- [Phenotypical Ontology Driven Framework for Multi-Task Learning](https://mlsanity.com/paper/2009.02188/) (2020)
- [Learning an Interpretable Graph Structure in Multi-Task Learning](https://mlsanity.com/paper/2009.05618/) (2020)

## Built on by (most cited first, 3 of 3)

- [Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation](https://mlsanity.com/paper/2210.10195/) (2022)
- [GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction](https://mlsanity.com/paper/2208.01220/) (2022)
- [Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics](https://mlsanity.com/paper/2302.02092/) (2023)

## Abstract (arXiv)

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.