謝佳亨: Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT

報告時間：2024年7月13日（星期六）上午9點

報告地點：工程管理與智能制造大樓1425會議室

報告人：謝佳亨助理教授

工作單位：特拉華大學

舉辦單位：管理學院

報告簡介：

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.

報告人簡介：

謝佳亨是特拉華大學阿爾弗雷德·勒納商學院會計與管理信息系統(tǒng)系的助理教授。他的研究興趣包括可解釋的深度學習、健康風險分析和商業(yè)分析。他的博士論文題為《基于大數(shù)據(jù)的健康風險分析：一種深度學習方法》，開發(fā)了新穎的深度學習方法來理解、預測和緩解三個層次的關鍵健康風險：患者行為風險、疾病風險和政策風險。他的研究成果已在許多頂級期刊上發(fā)表，包括MIS Quarterly、JMIS和JAMIA。

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