Severe complications have become major threats to chronic patients. Risk prediction models can assist with identifying patients’ risks of complications. Differing from those static prediction models (logistic regression, neural networks, 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. A generalized mixed prediction chain model (GMPC) is proposed that 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 former time to support the prediction of the response variable at the next time. The validity of GMPC is verified by internal cross-validation and external test, which demonstrate that GMPC significantly outperforms existing static prediction models and dynamic prediction modelslike generalized estimation equation (GEE) and generalized linear mixed model (GLMM)