Insulin optimization systems and testing methods with adjusted exit criterion accounting for system noise associated with biomarkers

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.

Description

technical field [0001] Embodiments of the present disclosure relate generally to diabetes management, and in particular to methods and systems for optimizing the insulin doses that diabetic patients take. Background technique [0002] Diabetes is a collection of metabolic diseases characterized by hyperglycemia due to insufficient insulin secretion, insulin function, or both. Diabetes manifests itself differently in each individual's body due to each individual's unique physiology interacting with different health and life style factors such as eating habits, weight, stress, illness, exercise and medication. A biomarker is a biologically derived indicator of a patient that is indicative of a biological or pathogenic process, pharmacological response, event or condition (eg, aging, disease or disease risk, presence or progression, etc.). For example, a biomarker can be an objective measure of a variable associated with a disease that can be used as an indicator or predictor...

AI Technical Summary

Problems solved by technology

Protocol noise can be generated by patient error due to inappropriate physical manipulation of the insulin delivery tool (e.g. syringe) or failure to measure blood glucose at the appropriate time

All sources of noise may lead to greater risk of adverse events such as hyperglycemic and hypoglycemic events, and system noise in particular leads to increased risk due to difficulties in controlling intrinsic physiological effects

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