报告时间：2024年7月13日（星期六）上午9点

报告地点：工程管理与智能制造大楼1425会议室

报告人：谢佳亨助理教授

工作单位：特拉华大学

举办单位：必赢线路检测中心

报告简介：

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.

报告人简介：

谢佳亨是特拉华大学阿尔弗雷德·勒纳商学院会计与管理信息系统系的助理教授。他的研究兴趣包括可解释的深度学习、健康风险分析和商业分析。他的博士论文题为《基于大数据的健康风险分析：一种深度学习方法》，开发了新颖的深度学习方法来理解、预测和缓解三个层次的关键健康风险：患者行为风险、疾病风险和政策风险。他的研究成果已在许多顶级期刊上发表，包括MIS Quarterly、JMIS和JAMIA。