報(bào)告題目：Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT
時(shí)間：2024年7月16日（周二） 10:00-11:30
地點(diǎn)：中關(guān)村校區(qū)主樓418
報(bào)告人：謝佳亨助理教授
報(bào)告人簡(jiǎn)介：
Jiaheng Xie is an Assistant Professor in the Department of Accounting and MIS at the University of Delaware’s Alfred Lerner College of Business and Economics. His research interests lie in interpretable deep learning, health risk analytics and business analytics. His dissertation, titled “Big Data-Based Health Risk Analytics: A Deep Learning Approach” develops novel deep learning methods to understand, predict and mitigate three levels of critically important health risks: patient behavioral risk, disease risk and policy risk. His prior works have been published at many premier journals, including MIS Quarterly, Journal of Management Information Systems (JMIS), and Journal of American Medical Informatics Association (JAMIA).
報(bào)告內(nèi)容簡(jiǎn)介：
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 prediction using motion sensor data. To connect human expertise in the decision-making, safeguard trust for this high-stake prediction, and ensure algorithm transparency, we develop an interpretable deep learning model: Temporal Prototype Network (TempPNet). TempPNet is built upon 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 the temporal progression of depression. Extensive empirical analyses using real-world motion sensor data show that TempPNet outperforms state-of-the-art benchmarks in depression prediction. Moreover, TempPNet interprets its predictions by visualizing the temporal progression of depression and its corresponding symptoms detected from sensor data. We further conduct a user study 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 prediction 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 of prediction outcomes and making informed interventions.
