讲座题目：Individualized Prediction of Depressive Disorder in the Elderly: A Multitask
Deep Learning Approach
主持人： 袁华 教授
Depressive disorder is one of the major public health problems among the elderly. An effective depression risk prediction model can provide insights on the disease progression and potentially inform timely targeted interventions. Therefore, research on predicting the onset of depressive disorder for elderly adults considering the sequential progression patterns is critically needed. This research aims to develop a state-of-the-art deep learning model for the individualized prediction of depressive disorder with a 22-year longitudinal survey data among elderly people in the United States. The experiments with the 22-year longitudinal survey data indicate that (a) machine learning models can provide an accurate prediction of the onset of depressive disorder for elderly individuals; (b) the temporal patterns of risk factors are associated with the onset of depressive disorder; and (c) the proposed multitask deep learning model exhibits superior performance as compared with baseline models. The results demonstrate the capability of deep learning-based prediction models in capturing temporal and high-order interactions among risk factors, which are usually ignored by traditional regression models. This research sheds light on the use of machine learning models to predict the onset of depressive disorder among elderly people. Practically, the proposed methods can be implemented as a decision support system to help clinicians make decisions and inform actionable intervention strategies for elderly people.
Qingpeng Zhang is an assistant professor with the school of data science at City University of Hong Kong. He received the B.S. degree in Automation from Huazhong University of Science and Technology, and the M.S. degree in Industrial Engineering and the Ph.D. degree in Systems and Industrial Engineering with a minor in Management Information Systems from The University of Arizona. Prior to joining CityU, he worked as a Postdoctoral Research Associate with The Tetherless World Constellation at Rensselaer Polytechnic Institute. He also worked at the Pacific Northwest National Laboratory and Chinese Academy of Sciences. His research interests include social informatics and social computing, complex networks, healthcare data analytics, data mining and semantic web. His work has been supported by the National Natural Science Foundation of China (PI), Health and Medical Research Fund of the Food and Health Bureau of Hong Kong SAR Government (PI), Guangdong Provincial Natural Science Foundation (PI), and the Theme-based Research Scheme of Research Grants Council (Co-I).