Co-Morbidity Exploration on Wearables Activity Data Using Unsupervised Pre-training and Multi-Task Learning

TL;DR

This paper introduces act2vec, an unsupervised learning method for wearable activity data, which improves disorder prediction by capturing co-occurrence and periodicity, and leverages multi-task learning to address co-morbidities.

Contribution

The paper presents a novel unsupervised representation learning technique, act2vec, for wearable data, and applies multi-task learning to enhance co-morbidity analysis in health monitoring.

Findings

act2vec outperforms traditional symbolic methods

Multi-task learning improves disorder prediction accuracy

Model generalizes well across diverse subjects

Abstract

Physical activity and sleep play a major role in the prevention and management of many chronic conditions. It is not a trivial task to understand their impact on chronic conditions. Currently, data from electronic health records (EHRs), sleep lab studies, and activity/sleep logs are used. The rapid increase in the popularity of wearable health devices provides a significant new data source, making it possible to track the user's lifestyle real-time through web interfaces, both to consumer as well as their healthcare provider, potentially. However, at present there is a gap between lifestyle data (e.g., sleep, physical activity) and clinical outcomes normally captured in EHRs. This is a critical barrier for the use of this new source of signal for healthcare decision making. Applying deep learning to wearables data provides a new opportunity to overcome this barrier. To address the problem of the unavailability of clinical data from a major fraction of subjects and unrepresentative subject populations, we propose a novel unsupervised (task-agnostic) time-series representation learning technique called act2vec. act2vec learns useful features by taking into account the co-occurrence of activity levels along with periodicity of human activity patterns. The learned representations are then exploited to boost the performance of disorder-specific supervised learning models. Furthermore, since many disorders are often related to each other, a phenomenon referred to as co-morbidity, we use a multi-task learning framework for exploiting the shared structure of disorder inducing life-style choices partially captured in the wearables data. Empirical evaluation using actigraphy data from 4,124 subjects shows that our proposed method performs and generalizes substantially better than the conventional time-series symbolic representational methods and task-specific deep learning models.