323 – Bayesian and Advanced Analytic Methods in Health Policy
Predictive Modeling of Inpatient Fall of Stroke Patients Using Electronic Medical Records Data
Ying Liu
iMEDacs
Cindy Jin
Princeton Pharmartech
Jeffrey Y. Zhang
Princeton Pharmatech
Predictive modeling of inpatient fall of stroke patients is challenging. Traditionally, logistic regression or survival models could be applied, but . This study presents a framework of analyzing discrete-time data as a binary classification problem. With its great flexibility of the novel framework, the potential relationship between clusters can be incorporated. Comparisons in experiments suggest that the proposed models consistently yield better predictive performances than classical statistical modeling for survival data. The proposed method is applied to electronic patient records data from 2007 to 2014 collected by Kessler foundation.