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 patientu0027s 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 xe2x80x9cpattern featurexe2x80x9d. 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.

Health Insurance Anomaly Detection Based on Dynamic Heterogeneous Information Network With the development of health insurance, the consequent problem of health insurance fraud has become increasingly serious. Outlier detection is a common method for health insurance anomaly detection. Researchers use some prior knowledge to assume patterns and indicators of interest. However, fraud pattern is concealed and changeable. The method of mining anomalies from fixed patterns is difficult to meet the current needs. In order to overcome this limitation, this paper combines the rich expression ability of heterogeneous information networks to model the complex relationships between entities, and establish a health insurance business representation model. The paper explores all possible business patterns, interrelated business portfolio patterns and related indicators in the field of health insurance. Considering the dynamic nature of a network, anomaly mining is carried out from both horizontal and vertical perspectives. Among them, the horizontal comparison adopts a fixed time period. The vertical comparison dynamically adjusts the time period according to the frequency of occurrence of the health insurance pattern instance, and then performs indicator calculation and outlier detection. Finally, the experimental results on the real data set show that our approach can narrow the scope of professional review and find more records of possible fraud than traditional methods.