Abstract: A system and method for using a graph-based data structure to capture complex relations between different healthcare entities, analyze and mine healthcare data. The system sets up a framework for analyzing and mining historical healthcare data to help clinical patients and practitioners to guide care and make early decisions for interventions. More particularly, the system and method use graph embedding and machine learning modeling to process healthcare data in order to match member/patients with healthcare facilities for performing a particular medical procedure needed by the patient.

Abstract: A system and method for using a graph-based data structure to capture complex relations between different healthcare entities, analyze and mine healthcare data. The system sets up a framework for analyzing and mining historical healthcare data to help clinical patients and practitioners to guide care and make early decisions for interventions. More particularly, the system and method use graph embedding and machine learning modeling to process healthcare data in order to match member/patients with healthcare facilities or providers for performing a particular medical procedure or other health intervention needed by the patient.

Abstract: A system according determines a machine learning based model for forecasting time series data for a given use case. The system determines a model metric for a specific use case of time series data. The system accesses a pool of machine learning based models including a plurality of machine learning based models machine learning based models based on different machine learning techniques. For each of the plurality of machine learning based models the system performs forecasting using the machine learning based model and determines the value of the model metric for the machine learning based model. The system selects a machine learning based model based on comparison of values of the model metric for machine learning based models. The system uses the selected machine learning based model for forecasting values for the time series data for the application.

Abstract: A system according to an embodiment optimizes communications with users using machine learning based models. The system receives user profile data for a set of users. For each user from the set of users, the system provides the user profile data as input to a machine learning based model and determines attributes describing the user, for example a measure of adherence rate for the user. The system ranks the set of users based on the predicted attributes. The system selects a subset of users from the set of users based on the ranking. For each selected user from the set of selected users, the system determines communication parameters for communicating with the selected user and sends a communication to the selected user based on the determined communication parameters.