Generalized mixed prediction chain model and its application in forecasting chronic complications

Abstract

The risk of severe complications poses a major threat to patients suffering from chronic diseases, and risk prediction models can assist with identifying patients’ risks of developing complications. Different from those static prediction models (logistic regression, decision trees, etc.) which use the patients’ cross-section data to predict whether they will suffer a complication, this research aims to provide a dynamic prediction method that comprehensively utilizes the patients’ longitudinal Electronic Health Records (EHR) to predict their complication progressions. The proposed generalized mixed prediction chain model (GMPC) takes the patient’s complication status as the response variable and takes the patient’s EHR data as the covariate. A mixed effect model is then employed to demonstrate the relationship between the response variable and the covariate. Additionally, to reduce the time delay between the historical EHR and the future complication status, GMPC constructs a prediction chain that uses the predicted value of the response variable at the previous time to support the prediction of the response variable at the next time. Internal cross-validation and external test verify the effectiveness of GMPC, and the results show that GMPC outperforms existing static prediction models and dynamic prediction models.