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

• URL: http://arxiv.org/abs/2203.12595v1
• Date: Sat, 19 Mar 2022 19:14:25 GMT
• Title: PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression
• Authors: Jiacheng Zhu, Gregory Darnell, Agni Kumar, Ding Zhao, Bo Li, Xuanlong Nguyen, Shirley You Ren
• Abstract summary: Heart rate variability (HRV) is a practical and noninvasive measure of autonomic nervous system activity. We develop Physiological Multitask-Learning (PhysioMTL) by harnessing Optimal Transport theory within a Multitask-learning framework.
• Score: 21.254400561280296
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Stressor Type Matters! -- Exploring Factors Influencing Cross-Dataset Generalizability of Physiological Stress Detection [5.304745246313982]
  This study explores the generalizability of machine learning models trained on HRV features for binary stress detection. Our findings reveal a crucial factor affecting model generalizability: stressor type. We recommend matching the stressor type when deploying HRV-based stress models in new environments.
  arXiv  Detail & Related papers  (2024-05-06T14:47:48Z)
• Physical formula enhanced multi-task learning for pharmacokinetics prediction [54.13787789006417]
  A major challenge for AI-driven drug discovery is the scarcity of high-quality data. We develop a formula enhanced mul-ti-task learning (PEMAL) method that predicts four key parameters of pharmacokinetics simultaneously. Our experiments reveal that PEMAL significantly lowers the data demand, compared to typical Graph Neural Networks.
  arXiv  Detail & Related papers  (2024-04-16T07:42:55Z)
• Simulation-based Inference for Cardiovascular Models [57.92535897767929]
  We use simulation-based inference to solve the inverse problem of mapping waveforms back to plausible physiological parameters. We perform an in-silico uncertainty analysis of five biomarkers of clinical interest. We study the gap between in-vivo and in-silico with the MIMIC-III waveform database.
  arXiv  Detail & Related papers  (2023-07-26T02:34:57Z)
• An optimized hybrid solution for IoT based lifestyle disease classification using stress data [2.3909933791900326]
  The proposed novel method employs a test that measures a subject's electrocardiogram (ECG), galvanic skin values (GSV), HRV values, and body movements. The developed approach is capable of dealing with the class imbalance problem by using WESAD (wearable stress and affect dataset) dataset.
  arXiv  Detail & Related papers  (2022-04-04T05:52:48Z)
• Personalized Stress Monitoring using Wearable Sensors in Everyday Settings [9.621481727547215]
  We explore objective prediction of stress levels in everyday settings based on heart rate (HR) and heart rate variability (HRV) We present a layered system architecture for personalized stress monitoring that supports a tunable collection of data samples for labeling, and present a method for selecting informative samples from the stream of real-time data for labeling.
  arXiv  Detail & Related papers  (2021-07-31T04:15:15Z)
• Continuous Decoding of Daily-Life Hand Movements from Forearm Muscle Activity for Enhanced Myoelectric Control of Hand Prostheses [78.120734120667]
  We introduce a novel method, based on a long short-term memory (LSTM) network, to continuously map forearm EMG activity onto hand kinematics. Ours is the first reported work on the prediction of hand kinematics that uses this challenging dataset. Our results suggest that the presented method is suitable for the generation of control signals for the independent and proportional actuation of the multiple DOFs of state-of-the-art hand prostheses.
  arXiv  Detail & Related papers  (2021-04-29T00:11:32Z)
• Self-supervised transfer learning of physiological representations from free-living wearable data [12.863826659440026]
  We present a novel self-supervised representation learning method using activity and heart rate (HR) signals without semantic labels. We evaluate our model in the largest free-living combined-sensing dataset (comprising >280k hours of wrist accelerometer & wearable ECG data)
  arXiv  Detail & Related papers  (2020-11-18T23:21:34Z)
• Learning Generalizable Physiological Representations from Large-scale Wearable Data [12.863826659440026]
  We present a novel self-supervised representation learning method using activity and heart rate (HR) signals without semantic labels. We show that the resulting embeddings can generalize in various downstream tasks through transfer learning with linear classifiers. Overall, we propose the first multimodal self-supervised method for behavioral and physiological data with implications for large-scale health and lifestyle monitoring.
  arXiv  Detail & Related papers  (2020-11-09T17:56:03Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling [121.50704279659253]
  We propose a cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations. We then use the distilled physiological features for robust multi-task physiological measurements. The disentangled features are finally used for the joint prediction of multiple physiological signals like average HR values and r signals.
  arXiv  Detail & Related papers  (2020-07-16T09:39:17Z)
• Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation [51.72616167073565]
  We propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation. We predict and fuse a pyramid of motion fields from multiple scales of feature representations to generate a more refined motion field. We then use a novel cyclic teacher-student training strategy to make the inference end-to-end and further improve the tracking performance.
  arXiv  Detail & Related papers  (2020-06-28T21:03:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.