Readmission Prediction for Patients with Heterogeneous Medical History: A Trajectory-Based Deep Learning Approach
Abstract:
Hospital readmission refers to the situation where a patient is re-hospitalized with the same primary diagnosis within a specific time interval after discharge. Hospital readmission causes $26 billion 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 patients' medical history 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 medical history. Our approach -- Trajectory-BAsed DEep Learning (TADEL) -- is motivated to tackle the deficiencies of the existing approaches by capturing dynamic medical history and accounting for patient heterogeneity. We evaluate TADEL on a five-year national Medicare claims dataset including 3.6 million patients per year over all hospitals in the United States, reaching an F1 score of 0.869 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.
Bio:
Dr. Zhang's primary research interests are social media analytics and machine learning. He is specifically interested in designing algorithms to analyze large social network and developing deep learning methods to investigate social media content such as text, image and video. His work has been published in premier IS journals such as MIS Quarterly, Information Systems Research, Journal of Management Information Systems, etc. His projects have been funded by federal agencies such as NSF and NIH.