Survival Prediction from Longitudinal Health Insurance Data using Graph Pattern Mining

Survival prediction based on health insurance data is a promising research direction, since through health insurance data, we may be able to uncover the implicit information reflecting the health status of patients. Deep learning models, especially the Recurrent Neural Networks (RNNs), are powerful tools. However, at this stage, it is often difficult to explain the semantics of the features and evolution laws learned by RNNs. Therefore, this paper proposes a survival prediction model based on graph pattern mining. First, each patient's health insurance data are built as a Heterogeneous Information Network (HIN). Then, frequent patterns are mined from these HINs and each frequent pattern is regarded as a feature, called "pattern feature". At last, the predictive survival time is given by an improved random forest, which is able to take into account the censored data. We conducted experiments on a real health insurance data set. The experimental results show that the model has better predictive ability than the traditional survival prediction models.