# US 10,307,109 B2 · Glucose predictor based on regularization networks with adaptively chosen kernels and regularization parameters > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,307,109 B2 - Document ID: US10307109B2 - Status: Granted - Kind: B2 - Application number: 14/112,311 - Filed: 2012-04-20 - Published: 2014-03-13 - Granted: 2019-06-04 - Earliest claimed: 2011-04-20 - Assignees: Novo Nordisk A/S - Applicants: Jette Randloev; Samuel McKennoch; Sergei Pereverzyev; Sivananthan Sampath - Inventors: Jette Randloev; Samuel McKennoch; Sergei Pereverzyev; Sivananthan Sampath - Web page: https://patentgrep.com/patent/US10307109B2 ## CPC - A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor} - A61B 5/14532: {for measuring glucose, e.g. by tissue impedance measurement} - A61B 5/4848: {Monitoring or testing the effects of treatment, e.g. of medication} - A61B 5/4866: {Evaluating metabolism (using breath test A61B5/083)} - A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems} - G16H 50/50: for simulation or modelling of medical disorders - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract The invention relates to a method and a device for predicting a glycaemic profile of a subject. A multistage algorithm is employed comprising a prediction setting stage specifying a functional space for the prediction and a prediction execution stage specifying a predicted future glycaemic state of the subject in the functional space as a continuous function of time. ## Claims ### Claim 1 A glucose prediction device comprising: an input device structured to receive information indicative of a physiologic condition of a subject; a processing device comprising an adaptive regularization network that is structured to predict a future glucose profile; an output device structured to convey the future glucose profile; and an alarm; wherein the future glucose profile is the glycaemic state of the subject as a continuous function of time; wherein the adaptive regularization network is adapted to perform a multistage prediction process and comprises a supervising learning machine and a supervised learning machine that allows the future glucose profile to be predicted with irregularly sampled data in the information received by the input device; wherein (a) the supervising learning machine is adapted to (i) compress the information received by the input device and (ii) run the compressed information through a pre-constructed machine to produce kernel parameters and a regularization parameter; and (b) the supervised learning machine is adapted to calculate the future glucose profile as a function of the information received by the input device and the kernel parameters and the regularization parameter produced by the supervising learning machine; and wherein the alarm is structured to alert the subject if the future glucose profile includes an impending hypo- or hyperglycaemic event. ### Claim 2 (depends on 1) The device according to claim 1, wherein the pre-constructed machine is produced by a linear fitting, and wherein the kernel parameters and the regularization parameter is based on the linear fitting. ### Claim 3 (depends on 1) The device according to claim 1, wherein the multistage prediction process comprises a reproducing kernel Hilbert space. ### Claim 4 (depends on 3) The device according to claim 3, wherein the reproducing kernel Hilbert space is selected based on a data pool of training data segments, wherein each training data segment is indicative of the physiologic condition of the subject at a point in time. ### Claim 5 (depends on 4) The device according to claim 4, wherein the data pool is obtained from a predetermined data pool. ### Claim 6 (depends on 4) The device according to claim 4, wherein the data pool is obtained from a continuously updated data pool. ### Claim 7 (depends on 1) The device according to claim 1, wherein the information indicative of the physiologic condition of the subject is at least one measurement of a body characteristic. ### Claim 8 (depends on 7) The device according to claim 7, wherein the body characteristic is blood or tissue glucose. ### Claim 9 (depends on 7) The device according to claim 7, wherein the input device is further adapted to receive information related to a therapeutic treatment. ### Claim 10 (depends on 9) The device according to claim 9, wherein the information related to the therapeutic treatment is past delivery of a glucose regulating agent. ### Claim 11 (depends on 1) The device according to claim 1, wherein the input device is further adapted to receive information related to a meal consumed or to be consumed by the subject. ### Claim 12 A computer-implemented method for predicting a future glucose profile of a subject that is the glycaemic state of the subject as a continuous function of time, the method comprising: providing a glucose prediction device comprising a processing device; receiving, by the processing device, information indicative of a physiologic condition of the subject; specifying, by the processing device, a functional space for the predicted glucose profile by compressing the received information and running the compressed information through a pre-constructed machine to produce kernel parameters and a regularization parameter; calculating, by the processing device, predicted glucose values as a continuous function of time based on the received information, the kernel parameters, and the initial regularization parameter to thereby produce the predicted future glucose profile of the subject; and alerting the user, by the glucose prediction device, if the future glucose profile of the subject includes an impending hypo- or hyperglycaemic event. ### Claim 13 (depends on 12) The method according to claim 12, further comprising producing the kernel parameters by a regularized learning algorithm in a reproducing kernel Hilbert space defined by at least approximately minimizing an error function. ### Claim 14 (depends on 12) The computer-implemented method according to claim 12, further comprising producing the pre-constructed machine by receiving a data pool of training data segments, wherein each training data segment is indicative of a physiologic condition of the subject at respective points in time; compressing each training data segment to obtain a corresponding set of compressed training data segments; determining a set of training parameters parameterising the functional space, wherein each training parameter of the training set minimizes a prediction error function indicative of a deviation between a physiologic condition and a predicted physiologic condition, wherein the predicted physiologic condition is predicted based on the functional space parameterised by said training parameter; and constructing, from the set of training parameters, a non-linear mapping between compressed input data segments and a set of kernel parameters parameterising the respective kernels. ### Claim 15 (depends on 14) The method according to claim 14, wherein constructing the non-linear mapping comprises determining a mechanism for determining a Tikhonov regularization parameter. ### Claim 16 (depends on 14) The method according to claim 14, wherein the prediction error function penalises a delay or failure in the prediction of physiological events classified as dangerous more heavily than a delay or failure in the prediction of physiological events classified as normal. ### Claim 17 (depends on 14) The method according to claim 14, wherein the data pool is obtained from a predetermined data pool. ### Claim 18 (depends on 14) The method according to claim 14, wherein the data pool is obtained from a continuously updated data pool. ### Claim 19 (depends on 12) The method according to claim 12, wherein the information indicative of the physiologic condition of the subject is at least one measurement of a body characteristic. ### Claim 20 (depends on 19) The method according to claim 19, wherein the body characteristic is blood or tissue glucose. ### Claim 21 (depends on 12) The method according to claim 12, further comprising receiving, by the processing device, information related to a therapeutic treatment. ### Claim 22 (depends on 21) The method according to claim 21, wherein the information related to the therapeutic treatment is past delivery of a glucose regulating agent. ### Claim 23 (depends on 12) The method according to claim 12, further comprising receiving, by the processing device, information related to a meal consumed or to be consumed by the subject. ### Claim 24 A glucose prediction device comprising: an input device structured to receive information indicative of a physiologic condition of a subject, wherein the information comprises blood or tissue glucose measurements; a processing device comprising an adaptive regularization network that is structured to predict a future glucose profile; an output device structured to convey the future glucose profile; and an alarm; wherein the future glucose profile is the glycaemic state of the subject as a continuous function of time; wherein the adaptive regularization network is adapted to perform a multistage prediction process and comprises a supervising learning machine and a supervised learning machine that allows the future glucose profile to be predicted with irregularly sampled data in the blood or tissue glucose measurements received by the input device; wherein (a) the supervising learning machine is adapted to (i) compress the information received by the input device and (ii) run the compressed information through a pre-constructed machine to produce kernel parameters and a regularization parameter; and (b) the supervised learning machine is adapted to calculate the future glucose profile as a function of the information received by the input device and the kernel parameters and the regularization parameter produced by the supervising learning machine; and wherein the alarm is structured to alert the subject if the future glucose profile includes an impending hypo- or hyperglycaemic event. ### Claim 25 (depends on 24) The device according to claim 24, wherein the pre-constructed machine is produced by a linear fitting, and wherein the kernel parameters and the regularization parameter is based on the linear fitting. ### Claim 26 (depends on 24) The device according to claim 24, wherein the multistage prediction process comprises a reproducing kernel Hilbert space. ### Claim 27 (depends on 26) The device according to claim 26, wherein the reproducing kernel Hilbert space is selected based on a data pool of training data segments, wherein each training data segment is indicative of the physiologic condition of the subject at a point in time. ### Claim 28 (depends on 27) The device according to claim 27, wherein the data pool is obtained from a predetermined data pool. ### Claim 29 (depends on 27) The device according to claim 27, wherein the data pool is obtained from a continuously updated data pool. ### Claim 30 (depends on 24) The device according to claim 24, wherein the input device is further adapted to receive information related to a therapeutic treatment. ### Claim 31 (depends on 30) The device according to claim 30, wherein the information related to the therapeutic treatment is past delivery of a glucose regulating agent. ### Claim 32 (depends on 24) The device according to claim 24, wherein the input device is further adapted to receive information related to a meal consumed or to be consumed by the subject. ### Claim 33 A computer-implemented method for predicting a future glucose profile of a subject that is the glycaemic state of the subject as a continuous function of time, the method comprising: providing a glucose prediction device comprising a processing device; receiving, by the processing device, information indicative of a physiologic condition of the subject, wherein the information comprises blood or tissue glucose measurements; specifying, by the processing device, a functional space for the predicted glucose profile by compressing the received information and running the compressed information through a pre-constructed machine to produce kernel parameters and a regularization parameter, wherein the kernel parameters are produced by a regularized learning algorithm in a reproducing kernel Hilbert space defined by at least approximately minimizing an error function; calculating, by the processing device, predicted glucose values as a continuous function of time based on the received information, the kernel parameters, and the initial regularization parameter to thereby produce the predicted future glucose profile of the subject; and alerting the user, by the glucose prediction device, if the future glucose profile of the subject includes an impending hypo- or hyperglycaemic event. ### Claim 34 (depends on 33) The method according to claim 33, further comprising producing the pre-constructed machine by receiving a data pool of training data segments, wherein each training data segment is indicative of a physiologic condition of the subject at respective points in time; compressing each training data segment to obtain a corresponding set of compressed training data segments; determining a set of training parameters parameterising the functional space, wherein each training parameter of the training set minimizes a prediction error function indicative of a deviation between a physiologic condition and a predicted physiologic condition, wherein the predicted physiologic condition is predicted based on the functional space parameterised by said training parameter; and constructing, from the set of training parameters, a non-linear mapping between compressed input data segments and a set of kernel parameters parameterising the respective kernels. ### Claim 35 (depends on 34) The method according to claim 34, wherein constructing the non-linear mapping comprises determining a mechanism for determining a Tikhonov regularization parameter. ### Claim 36 (depends on 34) The method according to claim 34, wherein the prediction error function penalises a delay or failure in the prediction of physiological events classified as dangerous more heavily than a delay or failure in the prediction of physiological events classified as normal. ### Claim 37 (depends on 34) The method according to claim 34, wherein the data pool is obtained from a predetermined data pool. ### Claim 38 (depends on 34) The method according to claim 34, wherein the data pool is obtained from a continuously updated data pool. ### Claim 39 (depends on 33) The method according to claim 33, further comprising receiving, by the processing device, information related to a therapeutic treatment. ### Claim 40 (depends on 39) The method according to claim 39, wherein the information related to the therapeutic treatment is past delivery of a glucose regulating agent. ### Claim 41 (depends on 33) The method according to claim 33, further comprising receiving, by the processing device, information related to a meal consumed or to be consumed by the subject.