3 citations · 4 across the 5 of their papers we have counts for
8 papers
HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data
Bang An, Amin Vahedian, Xun Zhou +2
Traffic accident forecasting is a significant problem for transportation management and public safety. However, this problem is challenging due to the spatial heterogeneity of the…
Gaussian Process Regression and Classification using International Classification of Disease Codes as Covariates
Sanvesh Srivastava, Zongyi Xu, Yunyi Li +2
International Classification of Disease (ICD) codes are widely used for encoding diagnoses in electronic health records (EHR). Automated methods have been developed over the years…
A predictive model for kidney transplant graft survival using machine learning
Eric S. Pahl, W. Nick Street, Hans J. Johnson +1
Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney don…
Personalized Cardiovascular Disease Risk Mitigation via Longitudinal Inverse Classification
Michael T. Lash, W. Nick Street
Cardiovascular disease (CVD) is a serious illness affecting millions world-wide and is the leading cause of death in the US. Recent years, however, have seen tremendous growth in t…
Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data
Amin Vahedian, Xun Zhou, Ling Tong +2
Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigatin…
Deriving Enhanced Geographical Representations via Similarity-based Spectral Analysis: Predicting Colorectal Cancer Survival Curves in Iowa
Michael T. Lash, Min Zhang, Xun Zhou +2
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use such models to explore differe…