Care for the Mind amid Chronic Diseases: An Interpretable AI Approach Using IoT

Health sensing for chronic disease management creates immense benefits for social welfare. Existing health sensing studies primarily focus on the prediction of physical chronic diseases. Depression, a widespread complication of chronic diseases is, however, understudied. We draw on the medical literature to support depression detection using motion sensor data. To connect humans in this decision making, safeguard trust, and ensure algorithm transparency, we develop an interpretable deep learning model: temporal prototype network (TempPNet). TempPNet is built on the emergent prototype learning models. To accommodate the temporal characteristic of sensor data and the progressive property of depression, TempPNet differs from existing prototype learning models in its capability of capturing temporal progressions of prototypes. Extensive empirical analyses using real-world motion sensor data show that TempPNet outperforms state-of-the-art benchmarks in depression detection. Moreover, TempPNet interprets its decision by visualizing the temporal progression of depression and its corresponding symptoms detected from sensor data. We further employ a user study and a medical expert panel to demonstrate its superiority over the benchmarks in interpretability. This study offers an algorithmic solution for impactful social good—collaborative care of chronic diseases and depression in health sensing. Methodologically, it contributes to extant literature with a novel interpretable deep learning model for depression detection from sensor data. Patients, doctors, and caregivers can deploy our model on mobile devices to monitor patients’ depression risks in real time. Our model’s interpretability also allows human experts to participate in the decision making by reviewing the interpretation and making informed interventions. This paper was accepted by D. J. Wu, information systems. Funding: J. Xie and X. Fang are supported by the University of Delaware Research Foundation Strategic Initiatives Grant and Alfred Lerner College of Business and Economics Research Grant, X. Zhao acknowledges financial support from the National Natural Science Foundation of China [Grant 72401172] and the Fundamental Research Funds for the Central Universities [Grant 2023110139, 2023110318]. J. Xie and X. Fang did not receive any form of support from, nor do they have any affiliation with, X. Zhao’s funding sources. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2023.04183 .