Abstract: A system for modeling the evolution of a system overtime using an advection-based process is provided. The system continuously evolves a probability density function (“PDF”) for a characteristic of a characteristic of the state of the system and its time-varying parameters. The PDF is evolved based on advection by solving an advection partial differential equation that is based on a system model of the system. The system model has time-varying parameters for modeling the characteristic of the state of the system. The system uses the continuously evolving PDF to make predictions about the characteristic of the state of the system.

Abstract: A system for modeling the evolution of a system over time using an advection-based process is provided. The system continuously evolves a probability density function (“PDF”) for a characteristic of a characteristic of the state of the system and its time-varying parameters. The PDF is evolved based on advection by solving an advection partial differential equation that is based on a system model of the system. The system model has time-varying parameters for modeling the characteristic of the state of the system. The system uses the continuously evolving PDF to make predictions about the characteristic of the state of the system.

Abstract: Systems, media, and methods for providing an interactive health portal for presentation of health information of an individual including: an animated three-dimensional avatar of the individual and at least one of the following distinct navigational modes for navigating a plurality of categories of health information, each category having at least one subcategory of health information, the modes including: a list navigational mode; a two-dimensional map navigational mode; and a three-dimensional landscape navigational mode; wherein the individual can switch between the navigational modes.

Abstract: A system for modeling the evolution of a system over time using an advection-based process is provided. The system continuously evolves a probability density function (“PDF”) for a characteristic of a characteristic of the state of the system and its time-varying parameters. The PDF is evolved based on advection by solving an advection partial differential equation that is based on a system model of the system. The system model has time-varying parameters for modeling the characteristic of the state of the system. The system uses the continuously evolving PDF to make predictions out the characteristic of the state of the system.

Abstract: A system for modeling the evolution of a system over time using an advection-based process is provided. The system continuously evolves a probability density function (“PDF”) for a characteristic of a characteristic of the state of the system and its time-varying parameters. The PDF is evolved based on advection by solving an advection partial differential equation that is based on a system model of the system. The system model has time-varying parameters for modeling the characteristic of the state of the system. The system uses the continuously evolving PDF to make predictions about the characteristic of the state of the system.

Abstract: Systems, media, and methods for providing an interactive health portal for presentation of health information of an individual including: an animated three-dimensional avatar of the individual and at least one of the following distinct navigational modes for navigating a plurality of categories of health information, each category having at least one subcategory of health information, the modes including: a list navigational mode; a two-dimensional map navigational mode; and a three-dimensional landscape navigational mode; wherein the individual can switch between the navigational modes.

Abstract: A system for generating a course of treatment (“COT”) recommender for recommending COTs for patients using machine learning is provided. A machine learning treatment recommendation (“MLTR”) system trains a COT recommender using training data that includes a feature vector and a label for each patient in a group of patients. The features of the feature vector may include features derived from patient data. A label is a course of treatment for a patient referred to as a labeling course of treatment. The MLTR system generates the training data from patient data collected over time. The MLTR system then uses the training data to train the COT recommender using a machine learning technique. Once the COT recommender has been trained, the COT recommender can be applied to a feature vector of patient data of a patient to generate an MLTR recommended course of treatment for the patient.