Roche Diabetes Care, Inc.

Roche Diagnostics Operations, Inc.

Roche Diagnostics Gmbh

F. Hoffmann-la Roche Ag

David L Duke

Abhishek S Soni

Alan Greenburg

Chinmay Uday Manohar

Steven Bousamra

Christian Ringemann

Bernd Steiger

Jürgen Rasch-Menges

Michael Brossart

Paul J Galley

Stefan Weinert

Mark Gilmore Mears

Eric R Diebold

Harvey B Buck, Jr

Phillip Edgar Pash

Nikolaus Schmitt

Timothy Peter Engelhardt

Matthew W Percival

Alain Bock

Steven A Bousamra

Erica Rose Diebold

Matt Percival

Erica R Diebold

Systems and methods for automatically displaying patterns in biological data may include one or more processors, and machine readable instructions. The machine readable instructions can cause the one or more processors to divide biological data into segments of interest. The one or more processers can transform, automatically, each of the segments of interest into a set of features according to a mathematical algorithm. Further, the one or more processers can cluster, automatically, the segments of interest into groups of clustered segments according to a clustering algorithm. The segments of interest can be grouped in the groups of clustered segments based at least in part upon the set of features. A cluster center can be associated with one of the groups of clustered segments. Moreover, the one or more processers can present, automatically, the cluster center on a human machine interface.

Systems and methods for automatically displaying patterns in biological monitoring data

Methods and devices involving using a bolus calculator to account for an uncertainty of a carbohydrate estimate to reduce a risk of hypoglycemia and/or a risk of hyperglycemia. More specifically, methods and devices using an algorithm executed by a processor of the bolus calculator and using a carbohydrate estimate and a measure defining the uncertainty of the carbohydrate estimate to determine the probability of hypoglycemia and/or hyperglycemia if the carbohydrate estimate is an overestimate or an underestimate, and subsequently providing recommendations and alerts to a user.

Bolus calculator with probabilistic carbohydrate measurements

A method for analysing physiological measurement values of a user is proposed. The method comprises at least one data acquisition step, wherein, during the data acquisition step, physiological measurement values of the user are acquired at different measurement times and stored in a measurement data record; at least one pattern selection step, wherein, during the pattern selection step, measurement values acquired during one comparison time interval are selected as at least one comparison pattern; and at least one pattern recognition step, wherein, during the pattern recognition step, patterns corresponding to the comparison pattern are sought after in the measurement data record.

Automatic recognition of known patterns in physiological measurement data

Methods and systems are disclosed for treating a person with diabetes by basal rate adjustment of insulin from a therapy delivery device based on risk associated with a glucose state of the person with diabetes. A method may include determining a current risk metric associated with a detected glucose state. The method may include determining a current risk metric associated with the detected glucose state based on a weighted average of cumulative hazard values of return paths generated from a glucose state distribution around a detected glucose state. The method may include calculating an adjustment to a basal rate of a therapy delivery device based on the current risk metric associated with the detected glucose state and a reference risk metric associated with a reference glucose level.

Risk-based control-to-range blood glucose diabetes management device

A system and method is provided for analyzing a glucose state. A method may include identifying a target glucose state and an initial glucose state. The method may include calculating a target return path for a transition from the initial glucose state to the target glucose state. The target return path may comprise at least one intermediate glucose state associated with the transition from the initial glucose state to the target glucose state. The target return path may be calculated based on a hazard associated with the at least one intermediate glucose state of the target return path.

System and method for assessing risk associated with a glucose state

A system and method for considering the effects of aerobic exercise on blood glucose levels for individuals is described. In at least one embodiment of the system of the present disclosure, the system comprises a computing device for generating a prediction of future blood glucose levels for the individual at least partly based on an exercise model, wherein the exercise model is based on parameters that are independent of intensity of the aerobic exercise, and a means for taking an action at least based on the prediction from the exercise model.

System and method considering the effect of physical activity on the glucoregulatory system

The present disclosure relates to a system and method for determining a basal rate adjustment based on risk associated with a glucose state of a person with diabetes. A method may include detecting a glucose state of the person based on a received glucose measurement signal and determining a current risk metric associated with the detected glucose state. The method may include identifying a reference glucose state and a reference risk metric associated with the reference glucose state, and calculating an adjustment to a basal rate of a therapy delivery device based on the current risk metric associated with the detected glucose state and the reference risk metric associated with the reference glucose level.

System and method for adjusting therapy based on risk associated with a glucose state

Methods and devices for retrospectively assessing continuous monitoring reference pattern data to determine a risk of a patient glucose level measurement taken in at least one data segment being outside a predetermined range. The methods and devices can include executing an algorithm to compare risk scores derived from reference pattern data in a currently collected data segment with risk scores of previously stored reference pattern data of previously collected data segments for a patient for assessing risk.

Hazard based assessment patterns

Methods and systems are disclosed analyzing a glucose level of a person having diabetes. The method, in at least one example, comprises receiving into a computing device a plurality of measured glucose values from a continuous glucose monitoring system coupled to a person having diabetes, analyzing the plurality of measured glucose values with a probability analysis tool on the computing device to determine a glucose threshold (g), and a boundary glucose value (g) at a probability threshold where the person having diabetes requires at least a predetermined insulin dose, and comparing, with the computing device, the boundary glucose value (g) to the glucose threshold (g), wherein if the boundary glucose value (g) is greater than the glucose threshold (g) then the computing device performs an alert on a user interface.

Methods and systems for analyzing glucose data measured from a person having diabetes

A method for analyzing physiological measurement values of a user is proposed. The method comprises at least one data acquisition step, wherein, during the data acquisition step, physiological measurement values of the user are acquired at different measurement times and stored in a measurement data record; at least one pattern selection step, wherein, during the pattern selection step, measurement values acquired during one comparison time interval are selected as at least one comparison pattern; and at least one pattern recognition step, wherein, during the pattern recognition step, patterns corresponding to the comparison pattern are sought after in the measurement data record.

A patient diabetes monitoring system with an efficient unsupervised daily monitoring profile clustering algorithm, a method, and a computer product thereof are disclosed. The system may include a physiological data input device or sensor which receives a plurality of physiological measurements to generate a dataset, a memory which stores a clustering algorithm, and a processor. The clustering algorithm when executed by the processor, causes the processor to automatically pre-process the dataset to control an amount of bias/aggressiveness from the collected unsupervised daily monitoring profiles, thereby generating a pre-processed dataset, build a similarity matrix from the pre-processed dataset, and output an optimum number of similarity clusters found by the processor from the similarity matrix.

Patient diabetes monitoring system with clustering of unsupervised daily CGM profiles (or insulin profiles) and method thereof

Methods and devices involving using a bolus calculator for calculating a correction insulin dose accounting for continuous glucose monitoring noise. More specifically, methods and devices using an algorithm executed by a processor of the bolus calculator and using a glucose measurement and uncertainty of the glucose measurement to determine if a standard correction dose should be adjusted once calculations are performed for the standard correction dose and hypoglycemia-averse correction insulin dose and the two doses are compared.

Bolus calculator with probabilistic glucose measurements

Methods and systems are disclosed for estimating a glucose level of a person having diabetes and selecting automatically open-loop and closed-loop control for a connected therapy delivery device. The method may comprise analyzing measured glucose results and corresponding impedance values received from a glucose sensor coupled to the person with a probability analysis tool implemented by a microcontroller to determine a total quality score that is based on the minimum constraint of a probability of glucose sensor accuracy determined measured glucose results and a probability of sensing quality determined from the impedance values. The microcontroller may estimate the glucose level of the person with a recursive filter based on the plurality of measured glucose results weighted with the total quality score and select automatically either open-loop control or closed-loop control for the connected therapy delivery device based on the value of the total quality score.

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Fishers, IN, United States of America

David L Duke