讲座题目：Hospital Readmission Prediction Using Trajectory-Based Deep Learning Approach

主讲人：Bin Zhang（张彬）is an assistant professor at Department of Management Information Systems, Eller College of Management, University of Arizona. He is also affiliated member of Artificial Intelligence Lab, University of Arizona. His research interests are Social Network Analysis, Analytical Methods for Large Social Networks, Statistical Modeling for Social Network Problems, Business Intelligence, Machine Learning and Bayesian Statistics.

时间：2018年7月26日10：00—11：30

地点：tyc234cc 太阳成集团313会议室

讲座简介：

Abstract: Hospital readmission refers tothe situation where a patient is re-hospitalized with the same primary diagnosis within a specific time interval after discharge. Hospital readmission causes $26 billion of preventable expenses to the U.S. health systems annually and often indicates suboptimal patient care. To alleviate those severe financial and health consequences, it is crucial to proactively predict patients’ readmission risk. Such prediction is challenging because the evolution of medical events (illness trajectory) is dynamic and complex. The state-of-the-art studies apply statistical models which assume homogeneity among all patients and use static predictors in a period, failing to consider patients’ heterogeneous illness trajectories. Our approach -TADEL(Trajectory-bAsed DEep Learning) – is motivated to tackle the problems with the existing approaches by capturing various illness trajectories and accounting for patient heterogeneity. We evaluated TADEL on a five-year national Medicare claims dataset including 3.6 million patients per year over all hospitals inthe United States, reaching an F1 score of 0.867 and an AUC of 0.884. Our approach significantly outperforms all the state-of-the-art methods. Our findings suggest that health status factors and insurance coverage are important predictors for readmission. This study contributes to IS literature and analytical methodology by formulating the trajectory-based readmission prediction problem and developing a novel deep-learning-based readmission risk prediction framework. From a health IT perspective, this research delivers implementable methods to assess patients’readmission risk and take early interventions to avoid potential negative consequences.