Personalized event detection methods and related devices and systems
First Claim
1. A method of detecting meals pertaining to operation of a medical device associated with a patient, the method comprising:
- obtaining, by a computing device via a network, a plurality of glucose measurements indicative of a glucose level in a body of the patient during an analysis interval;
obtaining, by the computing device, a patient-specific meal detection model that identifies one or more glucose measurement statistics that are correlative to occurrence of a meal for the patient;
determining, by the computing device, values for the one or more glucose measurement statistics characterizing the glucose level in the body of the patient within the analysis interval based on the plurality of glucose measurements;
determining, by the computing device, a meal probability associated with the analysis interval based on historical event data associated with the patient;
determining, by the computing device, a meal consumption metric based on the values for the one or more glucose measurement statistics and the meal probability using respective correlation coefficient values from the patient-specific meal detection model;
autonomously detecting, by the computing device, an occurrence of a meal during the analysis interval based on the meal consumption metric; and
in response to detecting the occurrence of the meal, providing, by the computing device, an indication of the occurrence of the meal associated with the analysis interval.
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Abstract
Medical devices and related patient management systems and event detection methods are provided. An exemplary method of detecting events pertaining to operation of a medical device, such as an infusion device, involves obtaining measurements indicative of a condition in a body of a patient, determining statistics for an analysis interval based on the measurements, and determining an event probability associated with the analysis interval based on historical event data associated with the patient. An event detection model associated with the patient is obtained and applied to the statistics and the event probability to identify occurrence of the event using the event detection model, and in response, an indication of the event associated with the analysis interval is provided, for example, by tagging or marking data in a database, displaying graphical indicia of the event, or the like.
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Citations
20 Claims
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1. A method of detecting meals pertaining to operation of a medical device associated with a patient, the method comprising:
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obtaining, by a computing device via a network, a plurality of glucose measurements indicative of a glucose level in a body of the patient during an analysis interval; obtaining, by the computing device, a patient-specific meal detection model that identifies one or more glucose measurement statistics that are correlative to occurrence of a meal for the patient; determining, by the computing device, values for the one or more glucose measurement statistics characterizing the glucose level in the body of the patient within the analysis interval based on the plurality of glucose measurements; determining, by the computing device, a meal probability associated with the analysis interval based on historical event data associated with the patient; determining, by the computing device, a meal consumption metric based on the values for the one or more glucose measurement statistics and the meal probability using respective correlation coefficient values from the patient-specific meal detection model; autonomously detecting, by the computing device, an occurrence of a meal during the analysis interval based on the meal consumption metric; and in response to detecting the occurrence of the meal, providing, by the computing device, an indication of the occurrence of the meal associated with the analysis interval. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 20)
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11. A method of detecting meals during to operation of an insulin infusion device associated with a patient, the method comprising:
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obtaining sensor glucose measurements for the patient; obtaining a bolus history for the patient; obtaining a meal detection model associated with the patient, the meal detection model identifying a predictive subset of glucose measurement statistics correlative to meals by the patient; calculating the predictive subset of glucose measurement statistics characterizing a glucose level of the patient for an analysis interval of the sensor glucose measurements; determining a meal probability associated with the analysis interval based on the bolus history; determining a meal consumption metric based on the calculated predictive subset of glucose measurement statistics for the analysis interval of the sensor glucose measurements and the meal probability associated with the analysis interval using respective correlation coefficient values from the meal detection model; autonomously detecting occurrence of a meal during the analysis interval based on the meal consumption metric; and in response to autonomously detecting occurrence of the meal, providing an indication of the occurrence of the meal associated with the analysis interval. - View Dependent Claims (12, 13, 14, 15, 16)
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17. A system comprising:
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a sensing arrangement to obtain glucose measurement values for a glucose level in a body of a patient; a database to store historical event data and a meal detection model associated with the patient; and a computing device communicatively coupled to the database and a network to; obtain the glucose measurement values; determine values for one or more glucose measurement statistics characterizing the glucose level in the body of the patient that are correlative to occurrence of a meal for the patient for an analysis interval based on the glucose measurement values; determine a meal probability associated with the analysis interval based on the historical event data associated with the patient; apply the meal detection model to the one or more glucose measurement statistics and the meal probability to determine a meal consumption metric based on the values for the one or more glucose measurement statistics and the meal probability using respective correlation coefficient values from the meal detection model and autonomously detect occurrence of a meal during the analysis interval based on the meal consumption metric; and provide an indication of the occurrence of the meal associated with the analysis interval. - View Dependent Claims (18, 19)
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Specification