Wearable Sensor-Based Chronic Condition Severity Assessment: An Adversarial Attention-Based Deep Multisource Multitask Learning Approach

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Publication History

Received: October 23, 2018
Revised: January 22, 2020; January 5, 2021; September 3, 2021
Accepted: September 30, 2021
Published Online as Articles in Advance: August 26, 2022
Published in Issue: September 1, 2022


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Advancing the quality of healthcare for senior citizens with chronic conditions is of great social relevance. To better manage chronic conditions, objective, convenient, and inexpensive wearable sensor- based information systems (IS) have been increasingly used by researchers and practitioners. However, existing models often focus on a single aspect of chronic conditions and are often “black boxes” with limited interpretability. In this research, we adopt the computational design science paradigm and propose a novel adversarial attention-based deep multisource multitask learning (AADMML) framework. Drawing upon deep learning, multitask learning, multisource learning, attention mechanism, and adversarial learning, AADMML addresses limitations with existing wearable sensor-based chronic condition severity assessment methods. Choosing Parkinson’s disease (PD) as our test case because of its prevalence and societal significance, we conduct benchmark experiments to evaluate AADMML against state-of-the-art models on a large-scale dataset containing thousands of instances. We present three case studies to demonstrate the practical utility and economic benefits of AADMML and by applying it to detect early-stage PD. We discuss how our work is related to the IS knowledge base and its practical implications. This work can contribute to improved life quality for senior citizens and advance IS research in mobile health analytics.

Additional Details
Author Shuo Yu, Yidong Chai, Hsinchun Chen, Scott J. Sherman, Randall A. Brown
Year 2022
Volume 46
Issue 3
Keywords Design science, deep learning, multitask learning, multisource learning, attention mechanism, adversarial learning, mobile health analytics
Page Numbers 1355-1394
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