Abstract: Systems and methods for building a run hypergraph for a process from parameter combinations subject to run constraints are provided. The run hypergraph comprises a plurality of nodes, and runs associated with nodes, each run comprising a run identifier and a parameter combination identifier. A process hypergraph comprising the nodes connected by process edges is obtained. Each edge specifies resource outputs of a parent node included in resource inputs of a child node. A plurality of factors and parameter combinations are identified. Each factor is associated with an input or output property of a resource input or output of a node, with a number of levels. Each parameter combination includes an instance of the factors, and for each factor, an associated property level. Each run constraint specifies a relationship between a number of runs of the parent and the child for a corresponding parent/child node pair.

Abstract: Embodiments of a testing method for optimizing a therapy to a diabetic patient comprise collecting at least one sampling set of biomarker data, computing a probability distribution function, a hazard function, a risk function, and a risk value for the sampling set of biomarker data wherein, wherein the probability distribution function is calculated to approximate the probability distribution of the biomarker data, the hazard function is a function which yields higher hazard values for biomarker readings in the sampling set indicative of higher risk of complications, the risk function is the product of the probability distribution function and the hazard function, and the risk value is calculated by the integral of the risk function, minimizing the risk value by adjusting the diabetic patient's therapy, and exiting the testing method when the risk value for at least one sampling set is minimized to an optimal risk level.

Abstract: Embodiments of a testing method for optimizing a therapy to a diabetic patient comprise collecting at least one sampling set of biomarker data, computing a probability distribution function, a hazard function, a risk function, and a risk value for the sampling set of biomarker data wherein, wherein the probability distribution function is calculated to approximate the probability distribution of the biomarker data, the hazard function is a function which yields higher hazard values for biomarker readings in the sampling set indicative of higher risk of complications, the risk function is the product of the probability distribution function and the hazard function, and the risk value is calculated by the integral of the risk function, minimizing the risk value by adjusting the diabetic patient's therapy, and exiting the testing method when the risk value for at least one sampling set is minimized to an optimal risk level.