Predicting glucose trends for population management
First Claim
1. One or more computer storage media storing computer-useable instructions, the instructions when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising:
- receiving a first set of glucose data at a prediction server, the first set of glucose data received from a plurality of sources including electronic medical records associated with a plurality of patients, one or more care facilities, one or more laboratories, or one or more integrated home devices;
generating, based on at least the glucose data, a training data set;
training, based on the training data set, one or more predictive models by determining one or more trends in the training data;
receiving, from a glucose monitor, a second set of glucose data corresponding to a patient;
determining, based on the second set of glucose data and utilizing the one or more predictive models, a real-time prediction indicating whether the patient is likely to have blood glucose levels corresponding to a predetermined threshold;
communicating, based on the determining, the real-time prediction and one or more interventions to a care team and the patient; and
automatically adjusting a frequency or dosage of medication dispensed by an integrated home device associated with the patient based on the one or more interventions.
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Accused Products
Abstract
Computerized systems and methods facilitate preventing dangerous blood glucose levels using a predictive model to predict whether a particular patient is trending to have dangerous blood glucose levels. The predictive model may be built using logistic or linear regression models incorporating glucose data associated with a plurality of patients received from a plurality of sources. The glucose data may include context data and demographic data associated with the glucose data and the plurality of patients. The predictive model may be employed to predict a likelihood of a particular patient to have dangerous blood glucose levels. Based on the likelihood, the prediction and one or more interventions are communicated to a care team or the patient. The one or more interventions may be incorporated into a clinical device workflow associated with a clinician on the care team or the patient.
23 Citations
18 Claims
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1. One or more computer storage media storing computer-useable instructions, the instructions when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising:
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receiving a first set of glucose data at a prediction server, the first set of glucose data received from a plurality of sources including electronic medical records associated with a plurality of patients, one or more care facilities, one or more laboratories, or one or more integrated home devices; generating, based on at least the glucose data, a training data set; training, based on the training data set, one or more predictive models by determining one or more trends in the training data; receiving, from a glucose monitor, a second set of glucose data corresponding to a patient; determining, based on the second set of glucose data and utilizing the one or more predictive models, a real-time prediction indicating whether the patient is likely to have blood glucose levels corresponding to a predetermined threshold; communicating, based on the determining, the real-time prediction and one or more interventions to a care team and the patient; and automatically adjusting a frequency or dosage of medication dispensed by an integrated home device associated with the patient based on the one or more interventions. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer-implemented method in a clinical computing environment comprising:
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receiving, via a first computing process, glucose data for a plurality of patients from a plurality of sources, the glucose data including context data and demographic data associated with the glucose data and the plurality of patients; generating, via a second computing process, a predictive model based on the glucose data using one or more logistic or linear regression models; employing, via a third computing process, the predictive model to predict a likelihood of a particular patient to have blood glucose levels corresponding to a predetermined threshold; communicating, via a fourth computing process, a prediction and one or more interventions to a care team and the patient based on the likelihood; automatically adjusting a frequency or dosage of medication dispensed by an integrated home device associated with the patient based on the one or more interventions; and incorporating, via a fifth computing process, the one or more interventions into a clinical device workflow associated with a clinician on the care team; wherein each of the computing processes is performed by one or more computing devices. - View Dependent Claims (12, 13, 14, 15, 16)
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17. A system comprising:
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a glucose database storing glucose data received for a plurality of patients from a plurality of sources, the glucose data including context data and demographic data associated with the glucose data and the plurality of patients; one or more processors; and one or more computer storage media storing instructions, the instructions when executed by the one or more processors, cause the one or more processors to; generate one or more predictive models based on the glucose data using one or more logistic or linear regression models; employ the one or more predictive models to predict a likelihood of a particular patient to have blood glucose levels corresponding to a predetermined threshold; communicate a prediction and one or more interventions to a care team or the particular patient based on the likelihood; and communicate a frequency or dosage of medication change to an integrated home device associated with the particular patient based on the one or more interventions. - View Dependent Claims (18)
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Specification